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Abstract Many studies have examined the impact of air pollution on cardiovascular hospitalization (CVH), but few have looked at the delayed effects of air pollution on CVH. Additionally, there has been no research on the spatial and temporal differences in how environmental pollutants affect CVH. This study seeks to identify spatial heteroscedasticity in the relation between PM2.5 and CVH by developing a Generalized Additive Distributed Lag (GADL) model. Data on hospitalization due to cardiovascular disease were collected from the Hospital Information System (HIS) of Mashhad University of Medical Science from 2017 to 2020. Air pollution data from 22 air quality monitoring (AQM) stations were obtained from the Environmental Pollution Monitoring Center of Mashhad administrates. Markov Random Field (MRF) smoother was utilized in the GADL model to account for spatial heteroscedasticity in the observations. This developed model is a Spatial Heteroscedastic Generalized Additive Distributed Lag (SHGADL) model. Our use of GADL allowed us to discover a significant relationship between PM2.5 exposures and the risk of CVH at lags 0 and 1 in all districts. Our results reveal heteroscedasticity in the Relative Risks (RR) of PM2.5 on CVH across different districts. After accounting for this spatial heteroscedasticity, we found that the RR of PM2.5 on CVH at lags 0 and 1 were 1.0102 (95% CI: 1.0034, 1.0170) and 1.0043 (95% CI: 1.0009, 1.0078) respectively. The central and southeastern districts showed higher RR for CVH. The developed SHGADL model provides evidence of a significant lagged effect of PM2.5 exposures on CVH, and identifies low- and high-risk districts for CVH in Mashhad. This finding can assist decision-makers in allocating resources and planning strategically, with a focus on local interventions to manage ambient air pollution and providing emergency care for CVH.

Background: Identifying factors that influence the length of hospital stay for suspected stroke patients is crucial for optimizing the utilization of hospital resources. This study aimed to determine the factors associated with the length of hospital stay for suspected stroke patients transferred to Qaem Hospital in Mashhad through emergency services using survival analysis. Methods: In this historical cohort study, general information was gathered for all suspected stroke patients who sought emergency services in Mashhad, the largest city in northeast Iran, from March 21, 2018, to March 20, 2019, and were then transferred to the Emergency Department of Qaem Hospital. Pre-hospital emergency data were integrated with hospital records using the mission ID. The primary outcome assessed in the study was the length of hospital stay, with model implementation carried out using the statistical software Stata. Results: The median hospitalization time until patients' recovery was seven days. Out of the 578 participants, 386 cases (66.8%) recovered, while the remaining 190 cases (33.2%) were censored (83 individuals had died during the study, and 107 individuals had exited the hospital for other reasons). The average age of patients at the time of hospitalization was 71.13±13.01 years. Statistical analysis employing Log-rank and Breslow tests identified a significant difference in hospitalization duration among patients receiving various levels of care and based on their insurance status. During multivariate analysis, the Cox regression model was considered unsuitable due to some variables not meeting the proportional hazards assumption, leading to the utilization of AFT models. Following the evaluation of AFT models, including Log-normal, Log-logistic, Exponential, and Weibull, the log-normal model emerged as the most suitable choice, exhibiting AIC and BIC values of 1273.909 and 1356.740, respectively. Significant variables influencing length of stay included patient admission priority, insurance status, season, and residency status. Conclusion: The study suggests that parametric survival models are effective for analyzing lifetime data. Additionally, in light of the significant variables identified, enhancing facility readiness and resource allocation could facilitate more efficient planning and implementation.

Abstract Over the past three years, the COVID-19 outbreak has become a major worldwide problem, affecting the health systems and economies of countries. The mean delays, the expected time to observe the average effect of the number of new cases on the number of deaths, are gold times for decision-making regarding disease control and treatment facilities to reduce the fatality rate. The interest of the present study is estimating the mean delays and adjusted fatality rates of COVID-19 with the new application of Distributed Lag Models (DLM) and their spatial distributions. The daily cases and deaths data of COVID-19 for 39 European countries was obtained from two sources; the "European Centre for Disease Prevention and Control" and the "Our World in Data" database. The mean delay and the Adjusted Fatality Rate (AFR) for each country at three-time intervals; the first and subsequent peaks before and after vaccination were estimated by the Distributed Lag Models. The spatial analysis was applied to find the spatial correlation of the mean delays and adjusted fatality rates among European countries. In the three-time intervals, the first and the subsequent peaks before vaccination, and after vaccination, the median and interquartile range of the mean delays; and AFRs were: 1.1 (0.4, 3.2); 0.024 (0.016, 0.044), 9.2 (6.2, 12.40); 0.013 (0.005, 0.020) and 7.3 (4.4, 11.0); 0.001 (0.001, 0.005), respectively. In the subsequent peaks before vaccination, the mean delays considerably increased, and the AFRs decreased for most European countries. After vaccination, the AFRs decreased considerably. Except for the first peak, the spatial correlations of AFRs were not significant among neighboring countries. Consecutive outcomes will occur with delays in outbreaks of infectious disease. Also, the fatality rates for these outcomes should be adjusted on delays. Estimating the mean delays and adjusted fatality rates by Distributed lag Models and the spatial distributions of theme in outbreaks showed that prevention and medical policies after the first peak as well as vaccination were effective to reduce the fatality rate of COVID-19, but these effects were different between countries. These results recommended policymakers and governments assign prevention and medical resources more effectively.

Abstract Background Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases. Methods The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model’s performance. Results Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%. Conclusion Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.

Abstract Background Stroke is the second leading cause of death in adults worldwide. There are remarkable geographical variations in the accessibility to emergency medical services (EMS). Moreover, transport delays have been documented to affect stroke outcomes. This study aimed to examine the spatial variations in in-hospital mortality among patients with symptoms of stroke transferred by EMS, and determine its related factors using the auto-logistic regression model. Methods In this historical cohort study, we included patients with symptoms of stroke transferred to Ghaem Hospital of Mashhad, as the referral center for stroke patients, from April 2018 to March 2019. The auto-logistic regression model was applied to examine the possible geographical variations of in-hospital mortality and its related factors. All analysis was performed using the Statistical Package for the Social Sciences (SPSS, v. 16) and R 4.0.0 software at the significance level of 0.05. Results In this study, a total of 1,170 patients with stroke symptoms were included. The overall mortality rate in the hospital was 14.2% and there was an uneven geographical distribution. The results of auto-logistic regression model showed that in-hospital stroke mortality was associated with age (OR = 1.03, 95% CI: 1.01–1.04), accessibility rate of ambulance vehicle (OR = 0.97, 95% CI: 0.94–0.99), final stroke diagnosis (OR = 1.60, 95% CI: 1.07–2.39), triage level (OR = 2.11, 95% CI: 1.31–3.54), and length of stay (LOS) in hospital (OR = 1.02, 95% CI: 1.01–1.04). Conclusion Our results showed considerable geographical variations in the odds of in-hospital stroke mortality in Mashhad neighborhoods. Also, the age- and sex-adjusted results highlighted the direct association between such variables as accessibility rate of an ambulance, screening time, and LOS in hospital with in-hospital stroke mortality. Thus, the prognosis of in-hospital stroke mortality could be improved by reducing delay time and increasing the EMS access rate.

Many studies have examined the impact of air pollution on cardiovascular hospitalization (CVH), but few have looked at the delayed effects of air pollution on CVH. Additionally, there has been no research on the spatial and temporal differences in how environmental pollutants affect CVH. This study seeks to identify spatial heteroscedasticity in the relation between PM2.5 and CVH by developing a Generalized Additive Distributed Lag (GADL) model. Data on hospitalization due to cardiovascular disease were collected from the Hospital Information System (HIS) of Mashhad University of Medical Science from 2017 to 2020. Air pollution data from 22 air quality monitoring (AQM) stations were obtained from the Environmental Pollution Monitoring Center of Mashhad administrates. Markov Random Field (MRF) smoother was utilized in the GADL model to account for spatial heteroscedasticity in the observations. This developed model is a Spatial Heteroscedastic Generalized Additive Distributed Lag (SHGADL) model. Our use of GADL allowed us to discover a significant relationship between PM2.5 exposures and the risk of CVH at lags 0 and 1 in all districts. Our results reveal heteroscedasticity in the Relative Risks (RR) of PM2.5 on CVH across different districts. After accounting for this spatial heteroscedasticity, we found that the RR of PM2.5 on CVH at lags 0 and 1 were 1.0102 (95% CI: 1.0034, 1.0170) and 1.0043 (95% CI: 1.0009, 1.0078) respectively. The central and southeastern districts showed higher RR for CVH. The developed SHGADL model provides evidence of a significant lagged effect of PM2.5 exposures on CVH, and identifies low- and high-risk districts for CVH in Mashhad. This finding can assist decision-makers in allocating resources and planning strategically, with a focus on local interventions to manage ambient air pollution and providing emergency care for CVH. [ABSTRACT FROM AUTHOR]

Abstract Background and Aims Infantile colic is one of the most common disorders in the first months of infants' lives. This condition hurts parents' moods. This study was carried out to investigate the effect of the topical use of olive oil on infantile colic symptoms. Methods The study was carried out on 80 infants 1–3 months old, randomly allocated into two groups, receiving abdominal massage with olive oil thrice a day in the intervention group (n = 40) and the same procedure with liquid paraffin in the placebo group (n = 40), for 14 successive days. During this period, the parents recorded the episodes of colic, duration of colic, crying intensity, and episodes of defecation. These variables were assessed and compared at the beginning and on the 7th and 14th days. Results The mean crying duration changed from 4.05 ± 2.44 to 1.41 ± 1.03 h/day (65% decrease) in the olive oil group and from 3.85 ± 1.37 to 1.60 ± 1.32 h/day (58% decrease) in the paraffin group (p = 0.38). The episodes of crying were reduced from 5.79 ± 4.56 to 2.51 ± 4.93 episodes/day (↓57%) in the olive group and from 6.01 ± 3.40 to 3.01 ± 2.40 episodes/day (↓50%) in the paraffin oil group (p = 0.14). Furthermore, the intensity of crying was decreased from 9.04 ± 1.54 to 4.48 ± 2.17 (p

ObjectiveGiven the recession and crises such as the Covid-19 Pandemic, tourism destinations and related businesses are attempting to continue to expose their tourism destinations and capacities to potential tourists. They also aim to encourage them by providing extra facilities to visit these tourism destinations once the crisis is over. Virtual reality technology seems to be an effective instrument for this purpose, which highlights the significance of using this technology in the present situation. Virtual reality is regarded as an ICT-enabled technology with the potential to provide a highly interactive experience for potential tourists and inspire them to visit these destinations in the future. This study aims to investigate the impacts of virtual reality technology on tourists’ behavioral intentions and the image of the tourism destination. MethodologyThe present study is descriptive-applied in nature. The required data will be collected using online questionnaire and available sampling method. The statistical population of the study includes all the tourists who usually use various mobile applications for their travels. For this purpose, a 360-degree virtual video from three popular tourism destinations in Khuzestan province were selected. The sample of this research includes 267 visitors. The reliability and validity of the research constructs were confirmed using Cronbach's alpha coefficient and mean extraction variance. Besides, research hypotheses were also tested using Smart-PLS3 software. FindingsThe results illustrate that “sense of presence” and “aesthetic experience” are the two the variables with a significantly positive impact on satisfaction with virtual reality. Moreover, satisfaction with virtual reality has a positive and significant effect on behavioral conflict and behavioral conflict has a significantly positive effect on the intention to visit the destination, verbal recommendation, and the destination image. ConclusionBased on the results of this study, virtual reality technology has a positive effect on tourist behavior and destination image. Accordingly, if virtual reality applications and programs can provide their users with a sense of presence in the scene, a more realistic experience, a more unique instances of aesthetic effects and principles, these users will experience greater satisfaction with the virtual travel experience. While attracting and motivating the users, this can lead to more engagement and interaction with the tourism experience. In addition, the higher the users are engaged with the experience and the more time and energy they spend interacting and participating in the virtual experience, the greater the likelihood of observing behavioral reactions. Therefore, higher engagement with the experience of visiting a destination virtually can help increase their intention to actually visit that destination in the future. Moreover, users who have had a more rewarding experience based on their engagement with the destination, will be more likely to share their experiences with others and recommend them. In addition, such a unique experience of interaction and engagement with the tourism destination and its attractions can creates a more desirable feeling toward the destination and increase the credibility and desirability of the destination. Eventually, it can also help improve the brand image of that tourism destination.

Introduction: Randomized controlled clinical trial studies are the most valuable types of studies that their proper design and accurate reporting are of particular importance in medical science. This study was performed with aim to critically evaluate the randomized controlled clinical trial studies in the Iranian Journal of Obstetrics, Gynecology and Infertility. Methods: In this cross-sectional analytic study, the clinical trial articles of the Iranian Journal of Obstetrics, Gynecology and Infertility between 2009 and 2019 were collected and scored based on the latest edition of the Consort Checklist (2010) by three biostatisticians that their training's adequacy was confirmed by measuring the kappa agreement coefficient. The articles were scored, and based on the scores, they were classified into five categories: very poor to excellent. Data were analyzed by SPSS software (version 25) and descriptive statistics. Results: Out of a total of 1318 articles published during 11 years, 325 were clinical trials, among them only 6 articles (1.85%) were rated as excellent. The lowest and highest scores among the reviewed articles were 14 and 28 (maximum score was 37). The mean and standard deviation of the scores obtained in all articles was calculated as 19.05± 0.34. However, less attention was paid to the two main issues of randomization and blindness in the clinical trials. Conclusion: Due to the importance of clinical trial studies, it is necessary to pay attention to the articles' quality based on the consort checklist. It is also suggested that authors of such articles pay more attention to the two essential foundations of clinical studies, randomization and blindness, to increase the generalization of the results.

Today the emergence of digital technologies has dramatically changed tourism industry. Among these modern technologies is Virtual Reality (VR). Nevertheless, VR technology has not been extremely used by tourist destinations in Iran, yet. So this study investigates Iranian attitude and behavioral intentions toward adoption VR technology in tourism. The sample of the study totaled 260 visitors from three different destinations by VR technology. Online questionnaire and convenience sampling method were used to collect data research. The research hypotheses were tested using smart PLS3 software. this research is among the first studies investigated Iranian attitude toward using VR, The results can be beneficial for managers and marketers of tourism destinations and help them in utilizing this technology as an effective advertising and promotional marketing tool.

Background and Objectives: The Covid-19 epidemic began in Wuhan, China in the late 2019 and became a global epidemic in March 2020. In this regard, one of the most important indicators of the healthcare systems is the in-hospital mortality rate, which occurs with a time lag of one to two weeks after hospitalization. The aim of this study was to investigate the relative risk of Covid-19 mortality considering this time lag according to the number of daily hospitalizations. Methods: The data included the number of daily hospitalizations and deaths from Covid-19 from 15 May 2020 to 10 February 2021 in Iran, which was obtained from the Github database. A log-linear distributed lag model was used to evaluate the relationship and lag effect between daily hospitalization and relative risk of death. Results: The mean number of daily hospitalizations and deaths were 1342.2 ± 7 731.5 and 190.6 11±118.6 in the study period, respectively. It was found that an increase in the number of daily hospitalizations had a significant relationship with an increase in the relative risk of death on the same day and in the following days. As the number of hospitalizations exceeded 2000 patients per day, the cumulative relative risk of death increased to more than one. Conclusion: The results showed that the number of hospitalizations exceeding 2000 people per day was an alert for the countrychr('39')s healthcare system. Overall, prevention and observance of health protocols in the first level followed by early diagnosis of the disease, improving the hospitals facilities and preparedness of healthcare staff can reduce the relative risk of death in the possible future peaks.

Background: Destination branding and social media strategy may be a major and strong marketing and promotional tool for destination marketers. Today tourism has been greatly affected by social media platforms. Besides being a major source of information, these tools can facilitate the interaction process among tourists and provide them with customized and personalized offers. These activities can ultimately lead to enhanced destination brand equity.Purpose: The present study attempts to investigate the impact of social media marketing on consumer-based brand equity components for tourism destinations.Method: The sample of the study totaled 149 visitors from Isfahan. Online questionnaires and convenience sampling methods were used to collect data research. The research hypotheses were tested using smart PLS software.Results: The results showed that there is a statistically positive and significant impact of social media marketing on destination brand awareness, destination brand image, destination brand quality, and destination brand loyalty.Conclusion: As the results show, social media marketing is an effective marketing tool to improve tourism marketing that in turn can enhance destination brand equity. These results are beneficial for tourism destination marketers and managers. Social media marketing as a prominent marketing tool can strengthen destination brand equity and potentially influence tourists’ behavior

Introduction. COVID-19, an epidemic of coronavirus infection, has become a major global threat. The coronavirus mainly targets the human respiratory system, followed by cytokine storm, and altered immune responses associated with disease progression and adverse outcomes. Sumac and pomegranate juice are rich in bioactive compounds, which potentially have antiviral activities. This study is aimed at investigating the effect of a diet based on the use of sumac and pomegranate juice on the treatment of outpatients with COVID-19. Methods. In this study, 182 outpatients with COVID-19 were randomly divided into two groups receiving a diet containing pomegranate juice and sumac along with standard treatment and the control group (group 2) receiving standard treatment. Results. Consumption of a diet containing pomegranate juice and sumac in outpatients with COVID-19, who were receiving standard-of-care treatment, led to a significant decrease in fever, chills, cough, weakness, smell and taste disorders, shortness of breath, diarrhea, nausea and vomiting, and abdominal pain compared with outpatients with COVID-19 who received only standard treatment. Conclusion. Clinical trials of outpatients have limitations such as patients’ resilience to post-COVID-19 follow-up. However, the use of pomegranate juice and sumac can be efficacious in reducing COVID-19 symptoms. This trial is registered with IRCT20190406043175N3.

Background and Objectives: Burns are tragic events with considerable mortality rates. This study was conducted to analyze the spatial pattern of burns and identify the factors associated with the burn injuries in Mashhad, Iran. Methods: This retrospective cross-sectional study was conducted in patients hospitalized in Imam Reza Hospital Burn Center, Mashhad, which is the only referral burn center in the northeast of Iran. Spatial generalized linear models (Poisson and negative binomial) were used to model the association between socio-economic characteristics and the incidence of burn injury. Results: A total of 1044 acute burn patients (69.9% male, 30.1% female) were reviewed. Most of the burned patients were in the age group 20-29 years (41.4%). The most common cause of burn injury was flame (64.6%). The local test of spatial autocorrelation confirmed that the burns had cluster pattern in the central areas of the city and the worn-out urban texture was at higher risk of burns. The results of model showed that the percentage of worn-out urban texture (IRR =1.007; 95%CI 1.005-1.008), literacy (IRR =0.98; 95% CI 0.95-0.99), percentage of employed women (IRR =0.96; 95%CI 0.92-0.99), unemployment rate (IRR =0.92; 95%CI 0.85-0.99) and percentage of margination (IRR =1.006; 95%CI 1.005-1.008) were associated with burns. Conclusion: Based on the findings, socio-economic and environmental characteristics are associated with the incidence of burn injuries in different areas. The results of this study could be used to design burn prevention strategies, especially in the high-risk areas and groups.

Background: Geospatial distribution analysis is a highly useful tool, especially in the field of health research and health economics. We aimed to study the geospatial distribution of poisoned patients and the risk factors in Mashhad, Iran. Methods: This was a retrospective cross-sectional study of patients treated at the Medical Toxicology Center, Imam Reza Hospital, Mashhad, Iran, which is the only referral center in northeast of Iran, in 2013. Negative binomial and Poisson approach via generalized linear mixed models were performed to investigate the association between socio-demographic characteristics with the number of reported cases of poisoning. Results: A total of 5064 poisoned patients (52% females) were included. Most of the poisoned patients were within the age group of 20-29 years old (41.4%). Pharmaceutical agents were the most common cause of poisonings (64.6%). The local test of spatial autocorrelation (Moran’s I) confirms that the poisoning had cluster pattern in Mashhad. Number of poisoning events were found to be associated with population density (RR= 1.00011; 95% CI 1.0001-1.00013), and the frequency of people with less than high school education (RR=1.49; 95% CI 1.32-1.68). Conclusion: The geospatial factors may have impact on the number of acute poisoning events in a city. Some parts of a city may exhibit spatial clustering in poisoning events. Once clusters are found, interventions can be focused to specific geographic locations and would be helpful for healthcare policymakers to focus on prevention programs.

BackgroundPublic health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis.MethodsDaily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)'s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed.ResultsThe results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; pConclusionThe Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

AbstractBackground and Purpose: Shoulder pain is a common complication from stroke that may cause some rehabilitation limits to achieve functional goals. The information regarding its prevalence and associated factors is limited. This study was conducted to investigate further details pertaining to its associated factors.Materials and Methods: This study was performed on 191 stroke patients at the Physical Medicine and Rehabilitation Ward at Shiraz-Medical School from 2001 to 2004. Each patient was followed up for one year. A questionnaire was prepared containing information about shoulder pain compliant, thorough history and physical examination, previous history of open heart surgery, scoliosis, limited neck range of motion, type of stroke (based on MRI and brain CT), osteoporosis (based on bone mineral density and X ray), shoulder adductor muscles and biceps spasticity (based on ashworth scale). In follow-up visits bi-monthly for one year, the questionnaire was filled out again and if present, shoulder pain was recorded.Results: Most patients developed shoulder pain between 2 and 6 months following their stroke. Forty-nine patients (32.2%) developed shoulder pain, 39(79.6%) of whom had spastic tone and 8 (16.3%) had flaccid tone. In both groups, with shoulder pain and without shoulder pain respectively, 6.7% and 1.9% had a history of open heart surgery, 6.1% and 1% had scoliosis, 67.3% and 35.1% had Intracranial hemorrhage, 73.5% and 23.3% had limited neck range of motion, and 24.5% and 35% had osteopenia.Conclusion: Spasticity was significantly more common in the shoulder pain group, which can be due to capsulitis, soft tissue inflammation (especially ligament and rotator cuff muscle) and anteroinferior subluxation of the shoulder. There was a significant statistical correlation between intracranial hemorrhage, reduced neck range of motion, and hemiplegic shoulder pain. It appears that treatment and rehabilitation of spasticity and underlying cervical area problems are helpful in preventing hemiplegic shoulder pain.J Mazand Univ Med Sci 2008; 18(65): 28-33(Persian)

Introduction: The relation between the anthropometric status of household members and food insecurity is complicated and not clearly defined. The current study aimed to examine the prevalence of food insecurity (FI) and its association with maternal and child anthropometric indices and dietary intake in a low-income district located in the northeast of Iran. Methods: This cross-sectional survey was conducted on 400 representative households. The participants were mothers of children aged ≤6 years. Food security with 18-item USDA questionnaire, anthropometric status of mothers and children was measured by measuring weight and height dietary intake with food frequency questionnaire. Results: Fifty-eight percent of households were food insecure. No correlation was denoted between food insecurity and anthropometric indices, except for maternal height (P=0.02). After adjustment for the other variables, food insecurity was inversely correlated with maternal age (OR: 1.12; 95% CI: 1.06- 1.19), maternal education level (OR: 8.41; 95% CI: 1.89-37.46), the employment status of the spouse OR: 4.28; 95% CI: 2.02-9.05), socioeconomic status (OR: 12.86; 95% CI: 4.84-34.16), and the number of children aged ≤6 years (OR: 2.83; 95% CI: 1.16-6.80). The mean carbohydrate, fat, energy and folic acid consumption were observed to be lower in the food insecure mothers (P<0.05), while the mean intake of fruits was higher in the food-secure mothers (P<0.001). Conclusion: Women in households with a low socioeconomic status were at an elevated risk of food insecurity and micronutrient deficiencies. [ABSTRACT FROM AUTHOR]