Translation and validation of the Persian version of the caring nurse-patient interaction scale (CNPI-23).

INTRODUCTION AND OBJECTIVES: Although there is great emphasis on nursing care interaction, there is a lack of knowledge about the quality of nurse-patient care interactions in Iran. The lack of knowledge is mainly related to a lack of short Persian instruments that measure nurse-patient interaction from a caring perspective. The present study aimed to validate a Persian version of the nurse and patient versions of the Caring Nurse-Patient Interaction scale (CNPI-23). METHODS: The scale (CNPI-23) was translated to Persian using the forward-backward translation method. After translation and re-translation, the scale was given to 15 nurses and faculty members of Kerman University of Medical Sciences, and CVI and CVR indices were calculated based on their opinions. The analytical cross-sectional study was conducted in Kerman/Iran in 2022. In this study, 230 working nurses and 230 hospitalized patients in hospitals affiliated with Kerman University of Medical Sciences were recruited using the convenience method to complete the 23-item Caring Nurse-Patient Interaction scale. Exploratory and confirmatory factor analyses were used to analyze the validity of the scale, and Cronbach's alpha and Raykov's rho indices were also calculated to evaluate internal consistency and composite reliability. Data were analyzed using R 4-1-2 software. RESULTS: The scale was completed by 230 nurses and 230 patients. It included four dimensions: humanitarian care, clinical care, comforting care, and communication care. The results of the content validity ratio (CVR) and content validity index (CVI) were acceptable for all items. The minimum value of reliability was 0.49. All the items were approved at the end of the content validity assessment. In the patients' scale, these four factors explained 81% of the total variance, and for the exploratory model, all the indices show the adequacy of the model. All factor loadings were significant and higher than 0.5. Raycov's rho and Cronbach's alpha indices for all numbers were higher than 0.7. The findings of the exploratory factor analysis showed that the nurses' scale reflected four caring domains, which explained about 62% of the total variance, and the results of Raycov's rho and Cronbach's alpha indices confirmed the final fit of this model. CONCLUSION: In general, the Persian version of the Caring Nurse-Patient Interaction scale has good validity and reliability and can be used to evaluate the quality of care interaction between Persian-speaking nurses and patients.

Correlation between nurses' attitude and practice toward communication with patients of decreased level of consciousness and its relationship with ethical care in ICU: A cross-sectional study.

Background and Aims: Communication between intensive care unit (ICU) nurses and patients with decreased Level of Consciousness (DLOC) is now regarded as a difficult task. Proper communication is regarded as a crucial component of care and stems from the attitude and practice of nurses. Intensive care nurses are responsible for providing care to patients with DLOC in accordance with ethical principles because care and ethics cannot be separated. To better understand ICU nurses' attitudes and practice toward communicating with patients who have decreased levels of consciousness and how this affects the provision of ethical care, the current study was conducted. Methods: This is a cross-sectional study. Two hundred ICU nurses from southeast Iran took part in this study. Three questioners, including attitude and practice toward communicating with patients who decreased Levels of consciousness, and ethical Care were used as data collection tools. Results: The mean score for ICU nurses' attitude, practice, and ethical care towards communicating with patients with DLOC were, respectively, 4.02 ± 0.45,2.91 ± 0.49. The findings demonstrated a very weak correlation between the ICU nurses' attitude score and communication practice for DLOC patients (p = 0.04; r = 0.14). Conclusion: Nurses who had a more positive attitude toward communicating with DLOC patients performed better in this area. It is suggested that nurses employed in ICUs should be monitored and judged based on how well they communicate with DLOC patients through workshops and conferences. By doing this, they would become more cognizant of the necessity for and effective methods of communication with these patients as well as the repercussions of their poor communication practice.

Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression.

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.

Efficacy and Safety of a Protein-Based SARS-CoV-2 Vaccine: A Randomized Clinical Trial.

Importance: The protein-based SARS-CoV-2 vaccines FINLAY-FR-2 (Soberana 02) and FINLAY-FR-1A (Soberana Plus) showed good safety and immunogenicity in phase 1 and 2 trials, but the clinical efficacy of the vaccine remains unknown. Objective: To evaluate the efficacy and safety of a 2-dose regimen of FINLAY-FR-2 (cohort 1) and a 3-dose regimen of FINLAY-FR-2 with FINLAY-FR-1A (cohort 2) in Iranian adults. Design, Setting, and Participants: A multicenter, randomized, double-blind, placebo-controlled, phase 3 trial was conducted at 6 cities in cohort 1 and 2 cities in cohort 2. Participants included individuals aged 18 to 80 years without uncontrolled comorbidities, coagulation disorders, pregnancy or breastfeeding, recent immunoglobulin or immunosuppressive therapy, and clinical presentation or laboratory-confirmed COVID-19 on enrollment. The study was conducted from April 26 to September 25, 2021. Interventions: In cohort 1, 2 doses of FINLAY-FR-2 (n = 13 857) or placebo (n = 3462) were administered 28 days apart. In cohort 2, 2 doses of FINLAY-FR-2 plus 1 dose of FINLAY-FR-1A (n = 4340) or 3 placebo doses (n = 1081) were administered 28 days apart. Vaccinations were administered via intramuscular injection. Main Outcomes and Measures: The primary outcome was polymerase chain reaction-confirmed symptomatic COVID-19 infection at least 14 days after vaccination completion. Other outcomes were adverse events and severe COVID-19. Intention-to-treat analysis was performed. Results: In cohort 1 a total 17 319 individuals received 2 doses and in cohort 2 5521 received 3 doses of the vaccine or placebo. Cohort 1 comprised 60.1% men in the vaccine group and 59.1% men in the placebo group; cohort 2 included 59.8% men in the vaccine group and 59.9% in the placebo group. The mean (SD) age was 39.3 (11.9) years in cohort 1 and 39.7 (12.0) years in cohort 2, with no significant difference between the vaccine and placebo groups. The median follow-up time in cohort 1 was 100 (IQR, 96-106) days and, in cohort 2, 142 (137-148) days. In cohort 1, 461 (3.2%) cases of COVID-19 occurred in the vaccine group and 221 (6.1%) in the placebo group (vaccine efficacy: 49.7%; 95% CI, 40.8%-57.3%) vs 75 (1.6%) and 51 (4.3%) in cohort 2 (vaccine efficacy: 64.9%; 95% CI, 49.7%-59.5%). The incidence of serious adverse events was lower than 0.1%, with no vaccine-related deaths. Conclusions and Relevance: In this multicenter, randomized, double-blind, placebo-controlled, phase 3 trial of the efficacy and safety of FINLAY-FR-2 and FINLAY-FR-1A, 2 doses of FINLAY-FR-2 plus the third dose of FINLAY-FR-1A showed acceptable vaccine efficacy against symptomatic COVID-19 as well as COVID-19-related severe infections. Vaccination was generally safe and well tolerated. Therefore, Soberana may have utility as an option for mass vaccination of the population, especially in resource-limited settings, because of its storage condition and affordable price. Trial Registration: isrctn.org Identifier: IRCT20210303050558N1.

PastoCovac and PastoCovac Plus as protein subunit COVID-19 vaccines led to great humoral immune responses in BBIP-CorV immunized individuals.

The optimal booster vaccine schedule against COVID-19 is still being explored. The present study aimed at assessment of the immunogenicity and antibody persistency of inactivated-virus based vaccine, BBIP-CorV and protein-subunit based vaccines, PastoCovac/Plus through heterologous and homologous prime-boost vaccination. Totally, 214 individuals who were previously primed with BBIBP-CorV vaccines were divided into three arms on their choice as heterologous regimens BBIBP-CorV/PastoCovac (n = 68), BBIBP-CorV/PastoCovac Plus (n = 72) and homologous BBIBP-CorV (n = 74). PastoCovac booster recipients achieved the highest rate of anti-Spike IgG titer rise with a fourfold rise in 50% of the group. Anti-RBD IgG and neutralizing antibody mean rise and fold rise were almost similar between the PastoCovac and PastoCovac Plus booster receivers. The antibody durability results indicated that the generated antibodies were persistent until day 180 in all three groups. Nevertheless, a higher rate of antibody titer was seen in the heterologous regimen compared to BBIP-CorV group. Furthermore, no serious adverse event was recorded. The protein subunit-based booster led to a stronger humoral immune response in comparison with the BBIP-CorV booster receivers. Both the protein subunit boosters neutralized SARS-CoV-2 significantly more than BBIP-CorV. Notably, PastoCovac protein subunit-based vaccine could be successfully applied as a booster with convenient immunogenicity and safety profile.

Population Size Estimation of People Who Use Illicit Drugs and Alcohol in Iran (2015-2016).

BACKGROUND: Estimating the number of people using illicit drugs and alcohol is necessary for informing health policy and programming. However, it is often challenging to reliably estimate the size of these marginalized populations through direct methods. In this study, we estimated the population size of these groups using the indirect Network Scale-Up (NSU) method in Iran from 2015 to 2016. METHODS: Using a self-administered questionnaire, we asked 15 124 individuals (54% men) about the number of people they know who used different types of drugs at least once in the past 12 months. Prevalence estimates were reported per 100 000 population. The uncertainty level (UL) was calculated using the bootstrap method. RESULTS: The average age of the respondents was 33 years old, and 35.1% of them were unmarried. The most common drugs and their prevalence were as follows: opium (2534 [95% UL: 2467-2598]), hashish (849 [95% UL: 811-886]), stimulants (methamphetamine, ecstasy pills, cocaine, and Ritalin) (842 [95% UL: 802-879]), heroin/crack (578 [95% UL: 550-607]), and drug injection (459 [95% UL: 438-484]). Additionally, we estimated the prevalence of alcohol use as 2797 (95% UL: 2731-2861). On average, substance use was 5.23 times more prevalent among men than women. Opium use was more prevalent among individuals aged >50 years old. Moreover, alcohol use was more prevalent among participants between 18 and 30 years old (5164 per 100 000 population). CONCLUSION: Although opium continues to be the most prevalent illicit drug in Iran, the patterns of illicit drug use are heterogeneous among different age groups, genders, and provinces. Age-gender specific and culturally appropriate interventions are warranted to meet the needs of people in different subgroups.

The Dynamic Effective Reproductive Number of COVID-19 during the Epidemic in Iran.

Background: We aimed to determine the generation time, the best model for estimating reproduction number (R), and to estimate the basic reproduction number (R0) and effective reproduction number (Rt) for COVID-19 in Iran. Methods: We used the daily incidence cases of COVID-19, hospitalized due to a probable diagnosis of COVID-19 from 19 February 2020 to 17 November 2020 in Iran. Four models, including maximum likelihood (ML), exponential growth (EG), time-dependent (TD), sequential Bayesian (SB) were evaluated. The weekly reproduction number with a 95% confidence interval (CI) was calculated. Results: TD model shows the best fit compared to other models for estimating reproduction number in Iran. The R0 in Iran in the first week of the epidemic, leading up to 21 February 2020 was 7.19, 95% CI: 5.56, 9.00. The lowest value for the Rt was equal to 0.77 between 3 to 10 March 2020 and 4 to 11 December 2020. From 11 June 2020 up to13 August 2020, the Rt was more than one but after then to 24 September 2021 was less than one. Conclusion: TD model was the best fit for estimating the R in Iran. The worst situation of the epidemic in Iran was related to the weeks leading up to 26 February 2020 and 28 October 2020, and better status was related to the weeks leading up to 10 March 2020 and 11 December 2020.

Factors affecting systolic blood pressure trajectory in low and high activity conditions.

Background: Typically, blood pressure dips during sleep and increases during daytime. The blood pressure trend is affected by the autonomic nervous system. The activity of this system is observable in the low and high activity conditions. The aim of this study was to assess the effect of individual characteristics on systolic blood pressure (SBP) across day-night under low and high activity conditions. Methods: The samples were 34 outpatients who were candidates for evaluation of 24 hours of blood pressure with an ambulatory. They were admitted to the heart clinic of Farshchian hospital, located in Hamadan province in the west of Iran. The hourly SBP during 24 hours was considered as a response variable. To determine the factors effecting SBP in each condition, the hidden semi-Markov model (HSMM), with 2 hidden states of low and high activity, was fitted to the data. Results: Males had lower SBP than females in both states. The effect of age was positive in the low activity state (ß=0.30; p<0.001) and negative in high activity state (ß= -0.21; p=0.001). The positive effect of cigarette smoking on SBP was seen in low activity state (ß=5.02; p=0.029). The overweight and obese patients had higher SBP compared to others in high activity state (ß=11.60; p<0.001 and ß=5.87; p=0.032, respectively). Conclusion: The SBP variability can be displayed by hidden states of low and high activity. Moreover, the effects of studied variables on SBP were different in low and high activity states.

Methods to Estimate the Average Social Network Size.

One of the requirements to calculate the size of a KP by the NSU method is to know the average social network size of the general population accurately (shown by C).

Estimating the Size of Hidden Groups.

NSU studies have been applied in different countries. For example, this technique has been used in the United States to estimate the number of women who had been raped in the last year.

Generalized linear mixed hidden semi-Markov models in longitudinal settings: A Bayesian approach.

Hidden Markov and semi-Markov models (H(S)MMs) constitute useful tools for modeling observations subject to certain dependency structures. The hidden states render these models very flexible and allow them to capture many different types of latent patterns and dynamics present in the data. This has led to the increased popularity of these models, which have been applied to a variety of problems in various domains and settings, including longitudinal data. In many longitudinal studies, the response variable is categorical or count-type. Generalized linear mixed models (GLMMs) can be used to analyze a wide range of variables, including categorical and count. The present study proposes a model that combines HSMMs with GLMMs, leading to generalized linear mixed hidden semi-Markov models (GLM-HSMMs). These models can account for time-varying unobserved heterogeneity and handle different response types. Parameter estimation is achieved using a Monte Carlo Newton-Raphson (MCNR)-like algorithm. In our proposed model, the distribution of the random effects depends on hidden states. We illustrate the applicability of GLM-HSMMs with an example in the field of occupational health, where the response variable consists of count values. Furthermore, we assess the performance of our MCNR-like algorithm through a simulation study.

The Impact of Control Measures and Holiday Seasons on Incidence and Mortality Rate of COVID-19 in Iran.

BACKGROUND: Preventive measures on the COVID-19 pandemic is an effective way to control its spread. We aimed to investigate the effect of control measures and holiday seasons on the incidence and mortality rate of COVID-19 in Iran. STUDY DESIGN: An observational study. METHODS: The daily data of confirmed new cases and deaths in Iran were taken from the Johns Hopkins University COVID-19 database. We calculated weekly data from 19 Feb to 6 Oct 2020. To estimate the impact of control measures and holiday seasons on the incidence rate of new cases and deaths, an autoregressive hidden Markov model (ARHMM) with two hidden states fitted the data. The hidden states of the fitted model can distinguish the peak period from the non-peak period. RESULTS: The control measures with a delay of one-week and two-week had a decreasing effect on the new cases in the peak and non-peak periods, respectively (P=0.005). The holiday season with a two-week delay increased the total number of new cases in the peak periods (P=0.031). The peak period for the occurrence of COVID-19 was estimated at 3 weeks. In the peak period of mortality, the control measures with a three-week delay decreased the COVID-19 mortality (P=0.010). The expected duration of staying in the peak period of mortality was around 6 weeks. CONCLUSION: When an increasing trend was seen in the country, the control measures could decline the incidence and mortality related to COVID-19. Implementation of official restrictions on holiday seasons could prevent an upward trend of incidence for COVID-19 during the peak period.

Evaluation of the treatment failure ratio in individuals receiving methadone maintenance therapy via the network scale up method.

BACKGROUND: The network scale up (NSU) method is an indirect size estimation method that has received remarkable attention in estimating the sensitive behaviors. Using methadone maintenance therapy (MMT), which is a treatment for opioid use disorder, is a sensitive topic in many societies. The aim of this manuscript was to estimate the number of MMT users and its failure ratio using the NSU method in the city of Kerman, Iran, in 2016. METHODS: In this cross-sectional study, 1275 men and 1275 women which were aged >18 years and who had lived in the city of Kerman for at least 5 years were recruited via multistage sampling. Data was collected via face-to-face interviews using a questionnaire that was included questions for estimating the network size of the residents of Kerman as well as the number of MMT users and the existing failure among them. RESULTS: In total, the average network size of the citizens of Kerman was 235; among them, 97 were men and 138 were women. Overall, the numbers of MMT users and failures were estimated at about 5289 and 2731, respectively, leading to a failure ratio about 52%. The treatment failure ratio in the women and men was equal to 55% and 51%, respectively. Among the men, the failure ratio in all age groups was equal to ˜50%. Among the women, the failure ratio in those who were aged <18 years was equal to 76%. CONCLUSION: The considerable failure ratio indicates the need for conducting studies on MMT services in order to understand the reasons which exist regarding this failure. This is of particular importance in specific groups, such as in young women. Further, the comparable results with other direct methods indicate that the NSU method could be used in the size estimation of MMT failure ratios.

Effects of Geranium Aromatherapy Massage on Premenstrual Syndrome: A Clinical Trial.

INTRODUCTION: This study evaluated the effects of geranium aromatherapy massage on premenstrual syndrome (PMS). METHODS: A total of 120 female students with diagnosed PMS were randomly divided into three groups as control group, aromatherapy massage group, and massage group. Essential oil of geranium 2% in almond oil and sweet almond oil were used in aromatherapy massage group and massage group, respectively. Aromatherapy massage protocol was run for 8 weeks. RESULTS: The study revealed that aromatherapy massage decreased the PMS physical and mental symptoms and massage therapy decreased the PMS physical and mental symptoms too (P < 0.001); however, this decrease was more in aromatherapy massage group (P < 0.001). CONCLUSIONS: This study recommends aromatherapy massage, as a complementary treatment, to improve PMS.

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.

National population size estimation of illicit drug users through the network scale-up method in 2013 in Iran.

BACKGROUND: For a better understanding of the current situation of drug use in Iran, we utilized the network scale-up approach to estimate the prevalence of illicit drug use in the entire country. METHODS: We implemented a self-administered, street-based questionnaire to 7535 passersby from the general public over 18 years of age by street based random walk quota sampling (based on gender, age and socio-economic status) from 31 provinces in Iran. The sample size in each province was approximately 400, ranging from 200 to 1000. In each province 75% of sample was recruited from the capital and the remaining 25% was recruited from one of the large cities of that province through stratified sampling. The questionnaire comprised questions on demographic information as well as questions to measure the total network size of participants as well as the network size in each of seven drug use groups including Opium, Shire (combination of Opium residue and pure opium), Crystal Methamphetamine, heroin/crack (which in Iranian context is a cocaine-free drug that mostly contains heroin, codeine, morphine and caffeine with or without other drugs), Hashish, Methamphetamine/LSD/ecstasy, and injecting drugs. The estimated size for each group was adjusted for transmission and barrier ratios. RESULTS: The most common type of illicit drug used was opium with the prevalence of 1500 per 100,000 population followed by shire (660), crystal methamphetamine (590), hashish (470), heroin/crack (350), methamphetamine, LSD and ecstasy (300) and injecting drugs (280). All types of substances were more common among men than women. The use of opium, shire and injecting drugs was more common in individuals over 30 whereas the use of stimulants and hashish was largest among individuals between 18 and 30 years of age. CONCLUSION: It seems that younger individuals and women are more desired to use new synthetic drugs such as crystal methamphetamine. Extending the preventive programs especially in youth as like as scaling up harm reduction services would be the main priorities in prevention and control of substance use in Iran. Because of poor service coverage and high stigma in women, more targeted programs in this affected population are needed.

Selection of Variables that Influence Drug Injection in Prison: Comparison of Methods with Multiple Imputed Data Sets.

BACKGROUND: Prisoners, compared to the general population, are at greater risk of infection. Drug injection is the main route of human immunodeficiency virus ý(HIV) transmission, in particular in Iran. What would be of interest is to determine variables that govern drug injection among prisoners. However, one of the issues that challenge model building is incomplete national data sets. In this paper, we addressed the process of model development when missing data exist. METHODS: Complete data on 2720 prisoners was available. A logistic regression model was fitted and served as gold standard. We then randomly omitted 20%, and 50% of data. Missing date were imputed 10 times, applying multiple imputation by chained equations (MICE). Rubin's rule (RR) was applied to select candidate variables and to combine the results across imputed data sets. In S1, S2, and S3 methods, variables retained significant in one, five, and ten imputed data sets and were candidate for the multifactorial model. Two weighting approaches were also applied. FINDINGS: Age of onset of drug use, recent use of drug before imprisonment, being single, and length of imprisonment were significantly associated with drug injection among prisoners. All variable selection schemes were able to detect significance of these variables. CONCLUSION: We have seen that the performances of easier variable selection methods were comparable with RR. This indicates that the screening step can be used to select candidate variables for the multifactorial model.

Estimating the Annual Incidence of Abortions in Iran Applying a Network Scale-up Approach.

BACKGROUND: Abortions are of major public health concern in developing countries. In settings in which abortion is highly prohibited, the direct interview is not a reliable method to estimate the abortion rate. The indirect estimation methods to measure the rate of abortion might overcome this dilemma; They are practical methods to estimate the size of the hidden group who do not agree to participate in a direct interview. OBJECTIVES: The aim of this study was to explore the practicality of an indirect method for estimating the abortion rate , Known as Network Scale-up, and to provide an estimate about the episode of abortion with and without medical indications (AWMI+ and AWMI-) in Iran. MATERIALS AND METHODS: This cross-sectional study was conducted in 31 provinces of Iran in 2012. A random sample between 200 and 1000 was selected in each province by the multistage sampling method that 75% of the data were collected from the capital and 25% from one main city. We selected samples from urban people more than 18 years old (12960) and we asked them about the number of abortion in women they knew who had experienced the medical and non-medical abortions in the past year. A range for the transparency factor was estimated based on the expert opinion. RESULTS: The range of the transparency factors for AWMI+ and AWOMI- were 0.43-0.75 and 0.2-0.34, respectively. Regarding the AWMI+, our minimum and maximum estimations (per 1000 pregnancies) were 70.54 and 116.9, respectively. The corresponding figures for AWMI- were 93.18, and 148.7. CONCLUSIONS: The frequency rates for AWMI+ and AWMI- were relatively high. Therefore, the system has to address to this hidden problem using the appropriate preventive policies.

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.

Influence of pattern of missing data on performance of imputation methods: an example using national data on drug injection in prisons.

BACKGROUND: Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern, to be addressed here, is the role of the pattern of missing data. METHODS: We used information of 2720 prisoners. RESULTS derived from fitting regression model to whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were lost. In scenario 1, we generated missing values, at above rates, in one variable which was significant in gold model (age). In scenario 2, a small proportion of each of independent variable was dropped out. Four imputation methods, under different Event Per Variable (EPV) values, were compared in terms of selection of important variables and parameter estimation. RESULTS: In scenario 2, bias in estimates was low and performances of all methods for handing missing data were similar. All methods at all missing rates were able to detect significance of age. In scenario 1, biases in estimations were increased, in particular at 50% missing rate. Here at EPVs of 10 and 5, imputation methods failed to capture effect of age. CONCLUSION: In scenario 2, all imputation methods at all missing rates, were able to detect age as being significant. This was not the case in scenario 1. Our results showed that performance of imputation methods depends on the pattern of missing data.