Iran to achieve the SDG 3.4 at national and sub-national levels.

The present study investigates different scenarios to project the chance of achieving SDG 3.4 in Iran. In this study, the Iranian Death Registry System data was employed to estimate the Unconditional Probability of Dying (UPoD) for the four major categories of NCDs; then, the Bayesian model averaging was used to project the UPoD at the national and sub-national levels. Also, the prevalence of the risk factors was projected by 2030 based on STEPs as well as some other study data. Plus, UPoD and the possibility of achieving the target were estimated once again based on the assumption that the global reduction in risk factors proposed by WHO would be adopted in Iran. The UPoDs for the four NCDs in Iran were 17.5% (95% UI: 16.3-19.2) and 14.7% (13.3-16.2) in 2010 and 2015 respectively and if the current trend continues, 2030 will mark the UPoD of 10.8% (7.9-14.3). However, If the risk factors are reduced to the WHO target level by 2030, the UPoDs will be reduced to 5.44% (3.51-7.39) and 6.55% (5.00-8.13) of the 2010 and 2015 baseline scenarios, respectively, to enable some provinces to meet SDG 3.4. If the current trend continues, Iran will and will not achieve the SDG 3.4 in 2010 and 2015 baseline scenarios, respectively. However, if the global target set for reducing risk factors is achieved, Iran will meet all expectations in SDG 3.4 except in Asthma and COPD. Therefore, effective interventions are recommended to be designed and followed to reduce Asthma and COPD.

Maternal factors associated with interbirth intervals in Australia: Results from a population-based longitudinal study.

BACKGROUND AND OBJECTIVE: Short and long intervals between successive births are associated with adverse birth outcomes, especially in low-income and middle-income countries, yet the birth intervals in high-income countries remain relatively understudied. The aim was to examine maternal factors associated with birth intervals in Australia. METHODS: The sample comprised 6130 participants in the Australian Longitudinal Study on Women's Health who were born in 1973-1978, had two or more births, and responded to regular surveys between 1996 and 2018. Interbirth interval (IBI) was defined as the time between successive live births. Maternal factors were examined using accelerated failure time models. RESULTS: For women with only two births (n = 3802), the median time to the second birth was 34.0 months (IQR 23.1, 46.2) with shorter IBI associated with higher socioeconomic status (eg, university education (31.9 months), less income stress (31.1)), and longer IBI associated with age over 35 (39.7), fair/poor health (43.0), untreated fertility problems (45.5), miscarriage (39.4), or abortion (41.0). For women with three or more births (n = 2328), the median times to the second and third births were 31.2 months (19.9, 42.1) and 36.5 months (25.3, 50.1), respectively; some factors were consistent between the first IBI and second IBI (eg, university education and being married were associated with shorter IBI), whereas income stress was associated with longer first IBI but not with second IBI. CONCLUSIONS: Understanding maternal factors associated with birth intervals in a high-income country like Australia may enable more nuanced tailoring of guidelines for prepregnancy care.

Data Smoothing, Extrapolation, and Triangulation.

While population size data at the national and subnational levels are critical for planning and resource allocation, most PSE studies are conducted in a few selected cities and towns due to resource and time constraints. Extrapolation methods to estimate the population size for locations not included in the PSE exercise are critical to providing data for all subnational jurisdictions and the entire country as a whole. The decision on how to select cities or towns that reflect best the national picture is a critical first step. You may need to include cities or towns from areas with low, medium, or high prevalence of your target population(s). Having some data from all these (three) areas increase the power of your study and helps to better extrapolate the results to unobserved areas, and to the national level. Through this process, data smoothing of subnational data is often needed due to relatively small samples size problem. In this chapter, we describe methods that are required for smoothing subnational data, model subnational data to extrapolate estimates to regional and national levels, and triangulate findings from different size estimation methods by a Bayesian framework to produce credible estimates.

Estimating the Loss in Expectation of Life and Relative Survival Rate among Hemodialysis Patients in Iran.

BACKGROUND: Information regarding the prognosis and burden of diseases can be used by policymakers to determine competing health priorities. We aimed to assess the Relative Survival Rate (RSR) and loss of expectation of life (LEL) to evaluate the prognosis and burden of diseases in Hemodialysis (HD) patients. STUDY DESIGN: A retrospective cohort study. METHODS: We recruited 648 HD patients referred to three referral centers in Kerman City, Iran, from 2008 to 2019. RSR, was defined as the ratio of the observed and the expected survival rates of general population for persons of the same age and sex as patients in the current study. LEL was determined as the difference between corresponding life expectancies (LE). The extended Cox proportional hazard model was used to identify variables associated with the outcome. RESULTS: Variables associated with outcome were diabetic status and age. In the 5th year of the follow-up study, the overall RSR was 0.57. In general, for HD patients, the estimation of LE and LEL was 22.6 and 12.36 year, respectively. CONCLUSION: HD patients, especially older patients, showed a very poor prognosis, with a large amount of lost life expectancy. Therefore, they need more care and attention from health authorities. It is suggested to estimate the cost of eliminating the risk factors causing kidney diseases.

Design, Development, and Evaluation of a Teleophthalmology System Using a Low-Cost Fundus Camera.

INTRODUCTION: The increasing prevalence of diabetic retinopathy in developing countries has become a worldwide concern. This problem is preventable by timely diagnosis and treatment; however, in the majority of cases, patients attend the eye clinics very late because of a lack of specialists and travel difficulties. Running a teleophthalmology system would significantly help to manage this disease. AIM: This study seeks to assess the accuracy of the teleophthalmology system and its effect on reducing unnecessary referrals in Iran. METHODS: This study was conducted on 125 diabetic patients. First, the patients were examined by a retina specialist using a slit lamp and, then, single-field digital photos were captured by a portable, low-cost fundus camera. The images were uploaded onto a website and, after two months, were assessed by two retina specialists and two general practitioners (GPs). Finally, the diagnoses based on the digital photos were contrasted with the diagnoses established through face-to-face visits as a gold standard. RESULTS: Out of 125 diabetic patients, eight (6.4%) were removed because of low-quality images and a total of 117 were evaluated. The sensitivity and specificity of each retina specialist presented with the photographs produced success rates of 90% and 97% respectively when judged against the gold standard of face-to-face visits. The rates of sensitivity for retinopathy referrals from the retina specialists were 92% and 85%. The sensitivity and specificity of their diagnoses of clinically significant macular edema (CSME) were calculated at 93% and 100%. The rates of sensitivity for each GP were 95% and 93% and the level of specificity was estimated to be approximately 98% for both GPs. The diagnosis rate for GPs when viewing the photographs as opposed to hosting face-to-face visits was more than 90%. Generally, with the implementation of this system, between 40% and 55% of referrals were calculated to have been avoidable. CONCLUSION: Our results from the first-ever research conducted on this topic in Iran showed that the teleophthalmology system is extremely accurate, that it can prevent unnecessary referrals and that it is useful for locating treatable patients. The results of this study could be of assistance in the running and expansion of such systems throughout Iran and Kerman Province to reduce eye damage arising from diabetes, decrease avoidable referrals to clinics, increase the availability of specialist visits for people in remote and rural areas and optimize the use of clinical infrastructures for patients in emergencies.

The Impact of Socio Economic Factors on the Adherence of Patients with Gestational Diabetes Mellitus to Medical Recommendations.

BACKGROUND: Adherence to medical recommendations is very important to control gestational diabetes mellitus (GDM), as one of the most important disorders during pregnancy. In this study, we explored the impact of socio-economic status (SES) on the adherence of a cohort of GDM in Iran. METHODS: In this prospective study, 230 pregnant women with confirmed GDM were followed from Feb to Jun 2013 in a referral diabetic care center in Iran. The SES of subjects were quantified using a combined score generated by principal component analysis (PSA). Medical adherence score of subjects was measured in three follow up visits in a range of 0 to 10 and were linked to SES using linear regression model. RESULTS: The adherence scores women in the first, second, and third follow up visits were 5.06±2.12, 5.46±2.06, and 5.08±1, respectively. Women fourth quartile of SES (the highest level of SES) has a least compliance to medical orders in comparison to first quartile of SES (the lowest level of SES) with the OR -2.75 (95% Cl: -3.17, -2.23). CONCLUSION: The medical adherence of pregnant women with GDM is significantly poorer in high SES groups. Therefore, as an important determinant, we may target high SES pregnant women to control the adverse effects of the disorder more efficiently.

Ergonomic Evaluation of Dental Professionals as Determined by Rapid Entire Body Assessment Method in 2014.

STATEMENT OF THE PROBLEM: In dentistry, incorrect working posture is the most important cause of musculoskeletal disorders. PURPOSE: The aim of this research was to evaluate the work postures of general dentists and specialists using rapid entire body assessment (REBA) method. MATERIALS AND METHOD: In this cross-sectional study, work postures were assessed in 90 dentists by employing REBA method. Stratified sampling method was used. Data were analyzed by analysis of variance (ANOVA), Independent t-test and Pearson's correlation test in SPSS 19. RESULTS: The results showed that work postures of 90% of dentists were at moderate- to high-risk levels. Among the specialists, periodontists, pedodontists and oral and maxillofacial surgeons had the worst body postures. CONCLUSION: In general, dentists' working postures need improvement and consequently, a more comprehensive ergonomic training and promotion is required in dentistry curriculum at Universities.

Estimation of the Basic Reproduction Number and Vaccination Coverage of Influenza in the United States (2017-18).

BACKGROUND: Determining the epidemic threshold parameter helps health providers calculate the coverage while guiding them in planning the process of vaccination strategy. Since the trend and mechanism of influenza is very similar in different countries, we planned a study with the mentioned goal by using data of US from 2017 to 2018. STUDY DESIGN: A secondary study. METHODS: R0 and corresponding vaccination coverage are estimated using the national and state-level data of the US from the 40th in 2017 to the 5th week in 2018. Four methods maximum likelihood (ML), exponential growth (EG), time-dependent reproduction numbers (TD), and sequential Bayesian (SB) are used to calculate minimum vaccination coverage. The gamma distribution is considered as the distribution and the generation of time. RESULTS: The peak of epidemy in most states has occurred in the 15th week after the beginning of the epidemics. The generation time obey the Gamma distribution with mean and standard deviation of 3.6 and 1.6, respectively, was utilized for the generation time. The R0 (vaccination coverage) equaled 1.94 (48.4%), 1.80 (44.4%), 3.06 (67.3%), and 2.11 (52.6%) for EG, ML, SB, and TD methods at the national level, respectively. CONCLUSION: The R0 estimations were in the range of 1.8-3.06, indicating that an epidemic has occurred in the US (R0>1). Thus, it is required to vaccinate at least 44.4% to 67.3% to prevent the next epidemics of influenza. The findings of this study assist futures studies to apply disease control by vaccination strategies in order to prevent a national disaster.

Exploring perceptions of policymakers about main strategies to enhance fertility rate: A qualitative study in Iran.

BACKGROUND: Total fertility rate in Iran has declined unprecedentedly over the past thirty years. However, debating on proper strategies to increase fertility is still a matter of discussion among experts. OBJECTIVE: To explain the main strategies to increase fertility from the viewpoints of the policy makers. METHODS: This is a qualitative study using content analysis. Purposeful sampling approach was used to gather data. The data were collected via semi-structured interviews. Eight experts participated in the study and the main criteria were executive experience related to public health, scientific publication in these areas and availability as well as their own interest. Content analysis was used to extract the codes. RESULTS: The main theme extracted was improving the infrastructures. Almost all participants agreed on interventions around removing marriage obstacles, improving working conditions for women, improving the quality of the educational system, training and consultation, research, and improving services to increase fertility rate. CONCLUSIONS: The government should formulate long-term instead of short-term policies, and note that improving the economic conditions along with the promotion of social welfare, and enabling women in balancing work and family, are highly influential in childbearing decision-making, as they ensure a better future for the next generation. In addition, people should touch on the potential risk of future fertility reduction, so it is suggested to inform the public through free discussions.

Can Religious Beliefs be a Protective Factor for Suicidal Behavior? A Decision Tree Analysis in a Mid-Sized City in Iran, 2013.

This study aimed to assess using tree-based models the impact of different dimensions of religion and other risk factors on suicide attempts in the Islamic Republic of Iran. Three hundred patients who attempted suicide and 300 age- and sex-matched patient attendants with other types of disease who referred to Kerman Afzalipour Hospital were recruited for this study following a convenience sampling. Religiosity was assessed by the Duke University Religion Index. A tree-based model was constructed using the Gini Index as the homogeneity criterion. A complementary discrimination analysis was also applied. Variables contributing to the construction of the tree were stressful life events, mental disorder, family support, and religious belief. Strong religious belief was a protective factor for those with a low number of stressful life events and those with a high mental disorder score; 72 % of those who formed these two groups had not attempted suicide. Moreover, 63 % of those with a high number of stressful life events, strong family support, strong problem-solving skills, and a low mental disorder score were less likely to attempt suicide. The significance of four other variables, GHQ, problem-coping skills, friend support, and neuroticism, was revealed in the discrimination analysis. Religious beliefs seem to be an independent factor that can predict risk for suicidal behavior. Based on the decision tree, religious beliefs among people with a high number of stressful life events might not be a dissuading factor. Such subjects need more family support and problem-solving skills.

Estimating the visibility rate of abortion: a case study of Kerman, Iran.

OBJECTIVES: Abortion is a sensitive issue; many cultures disapprove of it, which leads to under-reporting. This study sought to estimate the rate of abortion visibility in the city of Kerman, Iran-that is, the percentage of acquaintances who knew about a particular abortion. For estimating the visibility rate, it is crucial to use the network scale-up method, which is a new, indirect method of estimating sensitive behaviours more accurately. MATERIALS AND METHODS: This cross-sectional study was conducted in Kerman, Iran using various methods to ensure the cooperation of clinicians and women. A total of 222 women who had had an abortion within the previous year (74 elective, 74 medical and 74 spontaneous abortions) were recruited. Participants were asked how many of their acquaintances were aware of their abortion. Abortion visibility was estimated by abortion type. 95% CIs were calculated by a bootstrap procedure. A zero-inflated negative binomial regression analysis was conducted to assess the variables related to visibility. RESULTS: The visibility (95% CI) of elective, medical and spontaneous abortion was 8% (6% to 10%), 60% (54% to 66%) and 50% (43% to 57%), respectively. Women and consanguineal family were more likely to be aware of the abortion than men and affinal family. Non-family members had a low probability of knowing about the abortion, except in elective cases. Abortion type, marital status, sex of the acquaintance and closeness of the relationship were the most important determinants of abortion visibility in the final multifactorial model. CONCLUSIONS: This study shows the visibility rate to be low, but it does differ among social network members and by the type of abortion in question. This difference might be explained through social and cultural norms as well as stigma surrounding abortion. The low visibility rate might explain the low estimates of abortion rates found in other studies.

Assessment of the Importance of a New Risk Factor in Prediction Models.

BACKGROUND: Discovery of new risk factors poses new challenges on how to quantify their added value and importance in risk prediction improvement. OBJECTIVES: The aim of this study was to apply different statistics and to quantify the importance of some risk factors in acute myocardial infarction (AMI). PATIENTS AND METHODS: In a retrospective cohort study, 607 patients with AMI, aged more than 25 years were studied. They were admitted to the CCU of Imam Reza hospital in Mashhad, Iran from 2007 to 2012. Health information and death registration systems were used to identify patients and to assess their outcome. At first a model containing all variables was fitted (full model). Importance of variables was compared in terms of standardized regression coefficient and inclusion frequency in bootstrap samples. Then, a series of reduced models were fitted, where in each of them only one of the independent variables was excluded. Models were compared in terms of goodness of fit, accuracy (Cindex, R square), separation of patients into risk groups (SEP), and net reclassification improvement (NRI). RESULTS: Age was selected as the important factor based on all 7 statistics. Exclusion of age variable decreased C index from 0.75 to 0.68 and R square from 0.25 to 0.15. Duration of hospitalization was important based on 4 statistics. Exclusion of this variable decreased R square from 0.25 to 0.21. While gender was a useful variable in separation of patients into risk groups, its omission did not reduce model likelihood. The opposite was true in the case of using streptokinase during hospitalization. CONCLUSIONS: Our results revealed that a variable with high separation ability might not necessarily be useful in terms of goodness of fit. Therefore, importance should be defined carefully based on clinical objectives of the study.

Population-based survey on STI-associated symptoms and health-seeking behaviours among Iranian adults.

BACKGROUND: Qualified decision-making for the improved management of sexually transmitted infections (STIs) requires various sources of information. We aimed to estimate the STI-associated symptom prevalence and care-seeking patterns in the general population of Iran. METHODS: In 2014, using a street-based survey with a standard gender-specific questionnaire on STI-associated symptoms and willingness to seek treatment, we interviewed 9166 Iranian participants, who were selected from among the 18-60-year-old population using multistage sampling. Data were analysed via generalised estimating equation and survey analysis, taking into account a 95% confidence coefficient. RESULTS: About 67.3% of participants, mean age 33 years, were 'assumed sexually active' and were therefore eligible for inclusion. Approximately 39.9% (95% CI 28.4% to 51.4%) of women and 17.6% (95% CI 13.9% to 21.6%) of men reported at least one STI-associated symptom in the current week. The occurrence of symptoms decreased with an increase in age in both genders (p<0.05). About 21.2% (95% CI 13.3% to 29.1%) of women and 7.1% (95% CI 5.4% to 7.8%) of men treated symptoms themselves after symptoms first appeared. Of the women and men with symptoms, 37.4% (95% CI 24.8% to 50.0%) and 46.8% (95% CI 39.7% to 51.4%), respectively, sought care. Most women visited a gynaecologist and midwife; men tended to visit a general practitioner and urologist after their symptoms appeared. CONCLUSIONS: The prevalence of STI-associated symptoms in Iranian adults is considerable. The results emphasise the need for appropriate and timely STI care and more attention to sexual health promotion to mitigate onward and future infections. Attention to the care-seeking pattern is fundamental to policymaking and planning.

Comparison of conventional risk factors in middle-aged versus elderly diabetic and nondiabetic patients with myocardial infarction: prediction with decision-analytic model.

BACKGROUND: We sought to predict occurrence of myocardial infarction (MI) by means of a classification and regression tree (CART) model by conventional risk factors in middle-aged versus elderly (age ⩾65years) diabetic and nondiabetic patients from the Modares Heart Study. METHOD: A total of 469 patients were randomly selected and categorized into two groups according to clinical diabetes status. Group I consisted of 238 diabetic patients and group II consisted of 231 nondiabetic patients. Our population was MI positive. The outcome investigated was diabetes mellitus. We used a decision-analytic model to predict the diagnosis of patients with suspected MI. RESULTS: We constructed 4 predictive patterns using 12 input variables and 1 output variable in terms of their sensitivity, specificity and risk. The differences among patterns were due to inclusion of predictor variables. The CART model suggested different variables of hypertension, mean cell volume, fasting blood sugar, cholesterol, triglyceride and uric acid concentration based on middle-aged and elderly patients at high risk for MI. Levels of biochemical measurements identified as best risk cutoff points. In evaluating the precision of different patterns, sensitivity and specificity were 47.9-84.0% and 56.3-93.0%, respectively. CONCLUSIONS: The CART model is capable of symbolizing interpretable clinical data for confirming and better prediction of MI occurrence in clinic or in hospital. Therefore, predictor variables in pattern could affect the outcome based on age group variable. Hyperglycemia, hypertension, hyperlipidemia and hyperuricemia were serious predictors for occurrence of MI in diabetics.

Total and Partial Prevalence of Cancer Across Kerman Province, Iran, in 2014, Using an Adapted Generalized Network Scale-Up Method.

Due to the lack of nationwide population-based cancer registration, the total cancer prevalence in Iran is unknown. Our previous work in which we used a basic network scale-up (NSU) method, failed to provide plausible estimates of total cancer prevalence in Kerman. The aim of the present study was to estimate total and partial prevalence of cancer in southeastern Iran using an adapted version of the generalized network scale-up method. A survey was conducted in 2014 using multi-stage cluster sampling. A total of 1995 face-to-face gender-matched interviews were performed based on an adapted version of the NSU questionnaire. Interviewees were asked about their family cancer history. Total and partial prevalence were estimated using a generalized NSU estimator. The Monte Carlo method was adopted for the estimation of upper/lower bounds of the uncertainty range of point estimates. One-yr, 2-3 yr, and 4-5 yr prevalence (per 100,000 people) was respectively estimated at 78 (95%CI, 66, 90), 128 (95%CI, 118, 147), and 59 (95%CI, 49, 70) for women, and 48 (95%CI, 38, 58), 78 (95%CI, 66, 91), and 42 (95%CI, 32, 52) for men. The 5-yr prevalence of all cancers was estimated at 0.18 percent for men, and 0.27 percent for women. This study showed that the generalized familial network scale-up method is capable of estimating cancer prevalence, with acceptable precision.

Application of Random Forest Survival Models to Increase Generalizability of Decision Trees: A Case Study in Acute Myocardial Infarction.

BACKGROUND: Tree models provide easily interpretable prognostic tool, but instable results. Two approaches to enhance the generalizability of the results are pruning and random survival forest (RSF). The aim of this study is to assess the generalizability of saturated tree (ST), pruned tree (PT), and RSF. METHODS: Data of 607 patients was randomly divided into training and test set applying 10-fold cross-validation. Using training sets, all three models were applied. Using Log-Rank test, ST was constructed by searching for optimal cutoffs. PT was selected plotting error rate versus minimum sample size in terminal nodes. In construction of RSF, 1000 bootstrap samples were drawn from the training set. C-index and integrated Brier score (IBS) statistic were used to compare models. RESULTS: ST provides the most overoptimized statistics. Mean difference between C-index in training and test set was 0.237. Corresponding figure in PT and RSF was 0.054 and 0.007. In terms of IBS, the difference was 0.136 in ST, 0.021 in PT, and 0.0003 in RSF. CONCLUSION: Pruning of tree and assessment of its performance of a test set partially improve the generalizability of decision trees. RSF provides results that are highly generalizable.

Impact of socio-economic status on the hospital readmission of Congestive Heart Failure patients: a prospective cohort study.

BACKGROUND: The aim of this study was to examine the impacts of multiple indicators of Socio-economic Status (SES) on Congestive Heart Failure (CHF) related readmission. METHODS: A prospective study consisting of 315 patients without the history of admission due to CHF was carried out in Tehran during 2010 and 2011. They were classified into quartiles based on their SES applying Principal Component Analysis (PCA), and followed up for one year. Using stratified Cox regression analysis, Hazard Ratios (HRs) were computed to assess the impact of SES on the readmission due to CHF. RESULTS: During the 12 months follow-up, 122 (40%) were readmitted at least once. HR of lowest SES patients vs. the highest SES patients (the fourth versus first quartile) was 2.66 (95% CI= 1.51-4.66). Variables including abnormal ejection fraction (<40%), poor physical activity, poor drug adherence, and hypertension were also identified as significant independent predictors of readmission. CONCLUSION: The results showed low SES is a significant contributing factor to increased readmission due to CHF. It seems that the outcome of CHF depends on the SES of patients even after adjusting for some of main intermediate factors.

Assessment of psychometric properties of Persian version of Perceived Socio-cultural Pressure Scale (PSPS).

OBJECTIVES: To generate the Persian version of the Perceived Socio-cultural Pressure Scale. METHODS: The study, done in Kerman, Iran, from November 2010 to February 2011, comprised 1200 volunteers. After translation and back-translation, the questionnaire's internal consistency, criterion and construct validity were evaluated, individual and global scores of the Perceived Socio-cultural Pressure Scale were assessed between people with and without eating disorders. RESULTS: The mean scores for comparison were 14.7 +/- 6.64 and 21.84 +/- 10.65, giving a p-value of 0.0001. Internal and inter-item consistency were acceptable. Item-total correlation ranged from 54% to 80%. Construct and criterion validity of the scale were also acceptable. CONCLUSION: The Persian version of the Perceived Socio-cultural Pressure Scale is a competent tool for use in the general population and in individuals with eating disorders.

The estimation of active social network size of the Iranian population.

OBJECTIVES: The size of active network (C) of Iranian population is a very important parameter to estimate the size of unknown population using Network Scale Up (NSU) technique. However, there is little information about this parameter not only in Iran but also in other countries in Middle East region. Based on these needs, the aim of this paper is to estimate C for the Iranian population. METHODS: Based on available national statistics, 23 reference groups, with known population sizes were selected. Using multistage sampling method, 7454 individuals were recruited randomly around the country. We asked from our samples how many people they knew from each of the reference groups. Using NSU formulae, we maximized the goodness of fit of our estimation about the size of the reference groups by fitting the best C. However, the final C was set by excluding some of the reference groups with no added information; these inappropriate groups were selected by two techniques; regression, and ratio based approaches. RESULTS: Applying regression and ratio based approaches the estimated C was 308 and 380 respectively. The Pearson correlation coefficient between the real and estimated size of reference groups (based on our C) in both methods was above 0.95. However, results of ratio based had better performance. We saw that the network of males, singles, younger age groups, and those with higher education was larger than those in other groups. CONCLUSION: It seems that C in Iran is higher than that in developed countries, possibly because of its social structure. Because of cultural and social similarities in Middle East courtiers, C in other countries also might be higher than that in developed countries.

Assessment of trend and seasonality in road accident data: an Iranian case study.

BACKGROUND: Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. METHODS: This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. RESULTS: The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. CONCLUSION: Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.