Journal of research in health sciences最新文献

Background: This study aimed to investigate the effects of biopsychosocial interventions on non-specific chronic low back pain (NSCLBP) and disabilities caused by it among Students.

Study design: A two-group pretest-posttest randomized clinical trial.

Methods: The statistical population of the study was female students enrolled at the first-stage secondary school in Hamadan, Iran. A total of 200 students were selected through cluster sampling and randomized into two groups of intervention and control. The primary evaluation was performed by the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), the Health-Related Quality of Life (SF-36), the International Physical Activity Questionnaire-Short Form (IPAQ-S), the World Health Organization Disability Assessment Schedule (WHODAS), and the visual analogue scale (VAS). Upon developing and implementing the biopsychosocial model-based interventions for ten weekly two-hour sessions, the secondary evaluation was fulfilled, and the extracted data were analyzed using the IBM SPSS version 21.

Results: The independent-group t-test results revealed that the mean scores of quality of life (QOL) and physical activity significantly elevated in the intervention group, compared to the control. In addition, the mean value of disabilities, the amount of disorder in the lumbar region, and the VAS scores in the intervention group substantially declined compared to the control group.

Conclusion: The significant variations in the biopsychosocial factors demonstrated that the development of some interventions based on the bio-psychosocial model (BPSM) could help manage the NSCLBP and its ensuing disabilities. Therefore, the BPSM-based interventions could be exploited to minimize musculoskeletal disorders (MSDs) in students.

Background: Health is a fundamental issue in recent years, highlighting the importance of harmonizing the policies of any sector with health strategies. The present study aims to identify the socio-economic factors affecting health and to provide a cognitive map of the social determinants of health in Iran.

Study design: A retrospective cohort study.

Methods: This study follows a developmental process with an exploratory sequential mixed methods approach. First, a meta-synthesis qualitative method determines the most critical health determinants by reviewing 54 studies from 2000 to 2019. Then, the fuzzy cognitive map (FCM) is drawn based on interviews with six experts to derive the causal relationships among the social determinants of population health in Iran.

Results: Stage 1 introduces 170 factors as the health determinants, classified into four levels: individual, local, national, and global levels, with 7, 4, 13, and 3 branches, respectively. According to the causal relationships and the out-degree (od) index, the most effective factors are the economic system (18.24), governance and policy-making (17.13), and national policies (16.93). According to the degree of centrality, these factors are the economic system (33.27), health system (30.37), and governance and policy-making (30.15).

Conclusion: Considering health as a complex and comprehensive system, the resulting FCM displays that the policies developed in other sectors than health are profoundly affecting population's health in Iran. Specifically, the comparative analysis of this research shows that policies regarding the economic system and people's livelihood are more effective than the policies regarding the health system itself on the population's health in Iran.

Background: Road traffic injuries (RTIs) are one of the most critical factors that endanger human health. More specifically, head and neck injuries are the main causes of deaths and disabilities among motorcyclists. This study aimed to investigate the predictive factors of helmet use behavior among motorcyclists based on the theory of planned behavior (TPB).

Study design: This study followed the cross-sectional design.

Methods: This study was conducted on randomly selected 730 motorcyclist employees in Qom, Iran, in 2021. The data collection tool was a self-administered researcher-made questionnaire, including items on demographic characteristics, history of RTIs, and constructs of TPB. Data were analyzed using descriptive summary statistics, analysis of variance, independent samples t test, Pearson correlation coefficient, and structural equation modeling (SEM).

Results: In this study, only 9.8% of the participants reported that they always used a helmet while riding a motorcycle. About 60% reported a history of a motorcycle crash, and 11.5% had a history of head injuries. The direct effect of attitude, subjective norms, and perceived behavioral control on the intention to use a helmet were statistically significant, explaining 59% of the variation in behavioral intention (intention to use a helmet) (R²=0.59). Moreover, perceived behavioral control and behavioral intention had significant effects on helmet use behavior (R²=0.26).

Conclusion: The prevalence of helmet use among the studied population was very low. Moreover, TPB was useful in identifying the determinants of behavior and especially behavioral intention of helmet use among motorcyclists.

Background: Asbestos is a natural fiber leading to health risks like chronic lung diseases. The current study aimed to estimate pleural mesothelioma and lung cancer risk for population exposure to asbestos in Tehran, Iran.

Study design: A cross-sectional study.

Methods: According to the annual report of Air Quality Control Company (AQCC), from 2011-2020, carcinogenic risk and mesothelioma were assessed based on the Environmental Protection Agency (EPA) method using the Monte Carlo simulation (MCS). The relative risk (RR) of mortality cancer was calculated based on Camus and colleagues' model. Moreover, mesothelioma risk was estimated by Bourgault and colleagues' model.

Results: The mean concentration and health risk of asbestos in ambient air generally reduced from 2011 to 2020. The highest mortality risk for lung cancer was 8.4 per 100000 persons in 2011 and reduced to 1.8 in 2017. For mesothelioma, the corresponding values were 8.96 per 100000 persons in 2011 and reduced to 1.92 in 2017.

Conclusion: The findings of this study could be helpful to health policymakers in the management of asbestos risk.

Background: Hypertension and diabetes are common comorbidities in patients with COVID-19 and could be influencing the mortality of such patients. The present study aimed to evaluate the effects of hypertension alone and in comorbidity with diabetes on the death within 30 days among inpatients with COVID-19 in presence of well-known determinates of COVID-19 death.

Study design: A case-control study.

Methods: Four groups of COVID-19 inpatients including controls, diabetes alone, hypertension alone, and hypertension and diabetes comorbidities were defined. Each study groups did not have underlying diseases other than hypertension and diabetes. Demographic and general characteristics, underlying diseases, and hospital course events were extracted from medical records. The outcome of interest was alive at discharge/ death within 30 days after admission. Multivariable binary logistic analysis was employed to estimate the effect measures.

Results: The number of death within 30 days among controls (n=1359), diabetes alone (159), hypertension alone (406) and hypertension and diabetes comorbidities (188) were 12.68%, 15.72%, 20.74% and 26.74%, respectively. According to three multivariable analyses after adjusting older age, hospital length of stay, and intensive care unit (ICU) admission separately, the odds of death within 30 days in COVID-19 patients with having hypertension and diabetes comorbidities was 1.58, 2.13 and 1.91 times of patients without such comorbidities, respectively (P<0.015). The effect of hypertension alone was also significant after adjusting hospital length of stay and ICU admission but not for older age.

Conclusion: Our results suggest that comorbidities, such as hypertension and diabetes may be associated with COVID-19-related deaths independent of other underlying diseases, older age, and adverse hospital course events.

Background: The evaluation of the risk factors associated with the long-term survival rate of patients with myocardial infarction (MI) and the effects of discharge medications can significantly help select the most effective strategies for improving treatment.

Study design: A retrospective cohort study.

Methods: The participants of this retrospective cohort study were 21,181 patients who suffered from MI and were hospitalized in the cardiac care unit (CCU) of different public, private, and military hospitals in Iran from 20 March 2013 to 20 March 2014. Participants were followed up until February 2020 for any cardiovascular disease (CVD) mortality. To evaluate survival rate, the differences between groups, and the factors related to MI death, Kaplan-Meier, log-rank test, and Cox proportional-hazards model were used, respectively.

Results: One, three, five, and seven-year survival rates of patients were 88%, 81%, 78%, and 74%, respectively. Regarding the interaction effect of prescribed medical drugs, the highest 7-year survival rate of 86% (95% CI: 72%, 93%) was related to people who consumed anticoagulants, aspirin, clopidogrel, beta blockers, angiotensin-converting enzymes (ACEs), and angiotensin II receptor antagonist simultaneously. Considering the effect of other variables, the consumption of anticoagulants was associated with a decrease in survival rate (HR=1.13 CI: 1.06, 1.19).

Conclusion: As evidenced by the results of this study, different combinations of prescribed medication drugs had protective effects on long-term mortality compared to the group without any drug. Nonetheless, according to the drugs in each combination therapy, this protective effect ranged from HR=0.27 to HR=0.89. It is recommended that further studies compare the long-term effects of different drug combinations and also consider adherence to treatment in evaluating the effects of these combinations.

Background: Although a surge of interest has recently emerged in investigating the simultaneous problematic use of various technology-based tools, the findings are still inconclusive. The present web-based survey aimed at examining whether (a) personality traits, coping strategies, and sociodemographics are associated with problematic internet, smartphone, and SMS use among Greek users and (b) personality traits mediate the relationship between maladaptive coping strategies and problematic use of the three media.

Study design: A cross-sectional study.

Methods: A convenience and snowball sample of 1016 participants (84.4% female, mean age of 30.3 years) completed the Problematic Internet Use Questionnaire-9 (PIUQ-9), the Mobile Phone Problem Use Scale (MPPUS), the Self-Perception of Text Message Dependency Scale (STDS), the Personality Diagnostic Questionnaire 4+(PDQ-4+), and the Brief Coping Orientation to Problems Experienced Inventory (Brief COPE).

Results: Shared predictors between problematic use of the three technology-based tools were younger age and low educational level, the coping strategy of substance use, and the narcissistic, avoidant, and dependent personality disorders. Predictors of problematic internet and smartphone use were coping strategies of emotional support, self-distraction, and behavioral disengagement. According to structural equation models (SEM) models, cluster C personality disorders fully mediate the relationship between maladaptive coping strategies and problematic use of technology-based tools.

Conclusion: Addressing factors that predispose (i.e., personality traits), precipitate, or maintain problematic use (i.e., coping strategies) can lead to effective and cost-saving preventive (i.e., screening of vulnerable groups) and therapeutic efforts (i.e., teaching adaptive coping strategies).

Background: Traditional meta-analyses often assess the effectiveness of different doses of the same intervention separately or examine the overall differences between intervention and placebo groups. The present study aimed to model the effect sizes obtained from different doses in multiple studies using a two-stage dose-response meta-analytic approach while taking dose variations into account.

Methods: Different dose-response meta-analysis models using linear, quadratic, and restricted cubic spline (RCS) functions were fitted. A two-stage approach utilizing multivariate meta-analysis was performed and the obtained results were compared with those of the univariate meta-analysis. A random effect dose-response meta-analysis was performed using data from an existing systematic review on combination therapy with zonisamide and anti-Parkinson drugs for Parkinson's disease. The effective or optimum dose for producing maximum response was also investigated. Moreover, a sensitivity analysis was performed by changing the knots of the RCS model.

Results: Dose-response meta-analysis was performed using data from four double-blinded randomized controlled trials with 724 and 309 patients with Parkinson's disease in dose and placebo arms, respectively. The quadratic model yielded the smallest Akaike information criterion (AIC), compared to the linear and RCS models, indicating it to be the best fit for the data.

Conclusion: Compared to the traditional approach, the two-stage approach could model the dose-dependent effect of zonisamide on the Unified Parkinson's Disease Rating Scale (UPRDS) part III score and predict the outcome for different doses through a single analysis.

Background: This study aims to show the impact of imbalanced data and the typical evaluation methods in developing and misleading assessments of machine learning-based models for preoperative thyroid nodules screening.

Study design: A retrospective study.

Methods: The ultrasonography features for 431 thyroid nodules cases were extracted from medical records of 313 patients in Babol, Iran. Since thyroid nodules are commonly benign, the relevant data are usually unbalanced in classes. It can lead to the bias of learning models toward the majority class. To solve it, a hybrid resampling method called the Smote-was used to creating balance data. Following that, the support vector classification (SVC) algorithm was trained by balance and unbalanced datasets as Models 2 and 3, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model 1 that fitted traditionally.

Results: The prevalence of malignant nodules was obtained at 14% (n = 61). In addition, 87% of the patients in this study were women. However, there was no difference in the prevalence of malignancy for gender. Furthermore, the accuracy, area under the curve, and geometric mean values were estimated at 92.1%, 93.2%, and 76.8% for Model 1, 91.3%, 93%, and 77.6% for Model 2, and finally, 91%, 92.6% and 84.2% for Model 3, respectively. Similarly, the results identified Micro calcification, Taller than wide shape, as well as lack of ISO and hyperechogenicity features as the most effective malignant variables.

Conclusion: Paying attention to data challenges, such as data imbalances, and using proper criteria measures can improve the performance of machine learning models for preoperative thyroid nodules screening.

Background: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt.

Study design: A secondary study.

Methods: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios.

Results: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021.

Conclusion: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.