Journal of research in health sciences最新文献

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: Dangerous behaviors adversely affect the health of adolescents and young adults. This study aimed to identify the subgroups of college students based on the parameters of risky behavior and analyze the impact of demographic factors and internet gaming disorder (IGD) belonging to each class.

Study design: A cross-sectional study.

Methods: The study was conducted on 1355 students through a multi-stage random sampling method in 2020. A survey questionnaire was used to collect data, and all students completed 1294 sets of questionnaires. The data were analyzed using t test and latent class analysis (LCA) through SPSS and PROC LCA in SAS 9.2 software.

Results: Three latent classes have been identified as low-risk (75%), tobacco smoker (8%), and high-risk (17%). There was a high possibility of risky behavior in the third class. Marital status (being single) (OR = 2.28, 95% CI: 1.19-4.37), unemployment (having no job) along with education (OR = 1.56, 95% CI: 1.04-2.33), and IGD (OR = 1.06, 95% CI: 1.04-1.09) increased the risk of inclusion in the tobacco smoker class. Moreover, unemployment (having no job) along with education (OR = 1.43, 95% CI: 1.11-1.84) increased the chance of being in the high-risk class.

Conclusion: According to the findings of this study, 25% of the students were tobacco smokers or were in the high-risk class. The results of this study may help develop and evaluate preventive strategies that simultaneously take into account different behaviors.

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.

Background: It is of utmost importance to identify populations with an elevated risk for COVID-19 and the factors influencing its outcomes. The present study aimed to investigate factors affecting mortality and length of stay (LOS) among COVID-19 patients in the hospitals of East Azerbaijan province, Iran, during 15 months of this pandemic.

Study design: The present study followed a retrospective cohort design.

Methods: This retrospective study was conducted using data in the integrated syndromic surveillance system (ISSS) on patients admitted to the hospitals from February 21, 2020, to April 11, 2021. The association between variables of interest and death, as well as LOS, was investigated via multiple logistic regression and multiple linear regression analyses.

Results: In total, 24 293 inpatients with a mean age of 54.0 ± 19.4 years were included in this study. About 15% of them lost their lives, whose mean age was 69.0 ± 14.6 years, significantly higher than the recovered ones (P < 0.001). Factors, such as above 49 years of age (P < 0.001), male gender (OR = 1.17; 95% CI: 1.08-1.26), and having chronic diseases (OR = 1.32; 95% CI: 1.22-1.42), were correlated with patient mortality. In addition, having chronic diseases (Beta = 0.06; 95% CI: 0.03-0.08) was associated with higher LOS in hospitals.

Conclusion: In conclusion, older patients were at a higher risk of mortality and prolonged hospitalization. Furthermore, patients' underlying diseases could cause a severe form of COVID-19, which can lead to death and increase patients' LOS.

Background: Although the caregiving burden experienced by the family caregivers of drug addicts is receiving increased attention, there is still a need to study the possible predictors of the care burden, especially with the increasing numbers of addicts in Egypt and the important role of family caregivers in the support and treatment of addicts.

Study design: A cross-sectional study.

Methods: This study was conducted at Minia Hospital for Mental Health and Addiction Treatment, Egypt. Data was collected during interviews with addicts and their family caregivers. The caregiver burden was assessed using the Family Burden Interview Schedule (FBIS). The path analysis was used to assess the interrelationships between the burden and characteristics of addicts and caregivers.

Results: Based on the results, 96.7% of addicts were males, and their mean age was 28.8 ± 8.1 years, while their caregivers aged 39.7 ± 10.4 years and included 58.7% males. The caregivers reported a severe burden of care which was predicted by the addict's drug-related problems (B = 0.25, P = 0.0003), financial hardship (B = 0.46, P < 0.0001), and the caregiver's occupation (B = -0.16, P = 0.017). Financial hardship had an indirect association with the burden of care (B = 0.06, P = 0.041) mediated through drug-related problems score, which was predicted by the severity of dependence, admission for treatment, and the level of social support.

Conclusion: The burden of caring for addicts depends on patient-related problems, as well as caregivers' situations and income. Strategies to provide social support, financial aid, and problem-solving skills should be provided to the addicts and their caregivers as a part of treatment programs to help reduce the caregiving burden.

Background: Age at menarche affects women's health outcomes and could be a risk factor for some diseases, such as metabolic syndrome (MetS). We assessed the association between age at menarche and MetS components in women aged 35-70 in Hoveyzeh, southwest Iran.

Study design: A case-control study.

Methods: This case-control study was conducted on 5830 women aged 35-70 years in the Hoveyzeh cohort study (HCS), a part of the PERSIAN cohort study, from 2016-2018. The case group included women with MetS, while the controls were women without MetS. The MetS is determined based on standard NCEP-ATP III criteria. Data from demographic, socioeconomic, and reproductive history were gathered face-to-face through trained interviews. Moreover, laboratory, anthropometrics, and blood pressure measurements were assayed for participants. Multiple logistic regression was used to estimate the association between age at menarche and MetS, with adjustment for potential confounding variables.

Results: The mean age at menarche was 12.60 ± 1.76 years old. Urban and rural women differed in age at menarche (12.58 ± 1.71 and 12.63 ± 1.83 years, respectively). The study revealed a statistically significant relationship between MetS and menarche age. The odds of developing MetS were 14% higher in women with menstrual age ≤ 11 years than in other groups.

Conclusion: As evidenced by the results of this study, the odds of having MetS were higher in women whose menarche age was ≤ 11 years. Furthermore, the association between MetS components and age groups at menarche was statistically significant.

Background: Human papillomavirus (HPV) infection is a major cause of cervical cancer worldwide. Knowledge of the geographical distribution and epidemiology of the most common HPV genotypes is a crucial step in developing prevention strategies. Therefore, this study aimed to investigate HPV genotype distribution among HPV-positive women and men in Tehran, Iran.

Study design: A case series study.

Methods: The study was performed on 219 HPV-positive individuals (160 females and 59 males) from Tehran, Iran. Samples were obtained from the cervix and vagina of female subjects and the genital warts of male subjects. DNA was extracted from samples, and a polymerase chain reaction (PCR)-reverse dot blot genotyping chip was used to examine HPV genotypes. Formalin-fixed, paraffin-embedded tissue samples of 51 patients from the study population were also included in this study.

Results: The proportion of high-risk (HR)-HPV was 67.12%. The most common HR-HPV types were HR-HPV16 (17.4%), HR-HPV68 (11.4%), and HR-HPV51 (7.8%). The most common low-risk (LR)-HPV types included LR-HPV6 (31.1%), LR-HPV81 (11.9%), and LR-HPV62 (11.4%). The highest prevalence of HPV was in the age group of > 30 years (42.9%). Co-infection with multiple HR-HPV types was observed in 22.4% of specimens. Moreover, HR-HPV was found in 50% of women with normal cytology, 100% with a low-grade squamous intraepithelial lesion, and 84.61% with atypical squamous cells of undetermined significance.

Conclusion: The results indicated the remarkable growth of HR-HPV68, which has rarely been reported in Iran. The findings add knowledge to HPV epidemiological investigation and emphasize the need for introducing educational programs in high schools and appropriate vaccination in Iran.