Journal of Research in Health Sciences
Volume:22 Issue: 3, Summer 2022

Human papillomavirus (HPV) infection is a major cause of cervical cancer worldwide. Knowledge about the geographical distribution and epidemiology of the most common HPV genotypes is a crucial step in developing prevention strategies. We aimed to investigate the HPV genotype distribution among HPV positive women and men. Study design: Cross-sectional study.

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

The frequency of high-risk (HR)-HPV was 67.12%. The most common HR-HPV type was HR-HPV16 (17.4%), HR-HPV68 (11.4%), and HR-HPV51 and HR-HPV53 (both 7.8%). The most common low-risk (LR)-HPV included LR-HPV6 (31.1%), LR-HPV81 (11.9%), and LR-HPV62 (11.4%). The highest prevalence of HPV was in the age group >30 (42.9%). Co-infection with multiple HR-HPV types was observed in 22.4% of specimens. HR-HPV was found in 50% of women with normal cytology, 100% with low-grade squamous intraepithelial lesion, and 84.61% with atypical squamous cells of undetermined significance cells.

Our findings indicated remarkable growth of HR-HPV68, which has rarely been reported in Iran. Considering the high prevalence of HPV in people younger than 30 years old, it seems necessary to introduce educational programs in high schools to increase awareness about ethical issues related to human health.

Although the caregiving burden for drug addicts among their family members is receiving increased attention, there is still a need to study the possible predictors of burden to develop intervention strategies and support addicts and caregivers, especially with the increasing number of addicts worldwide.Study design: A cross-sectional study.

The study was conducted among 150 pairs of addicts and their family caregivers at Minia Hospital for Mental Health and Addiction Treatment. Path analysis was used to build interrelationships between the caregiver burden and addict and caregiver attributes. The caregiver burden was assessed using the Family Burden Interview Scale (FBIS). While, the addict patients completed the Severity of Dependence Scale (SDS), Drug Abuse Screening Test (DAST–20), Perceived Devaluation Discrimination scale (PDD), and Social Support Questionnaire short form (SSQ6).

The caregivers reported a severe burden of care which was predicted by the addict’s drug-related problems (B= 0.25), financial hardship (B= 0.46), and the caregiver’s occupation (B= -0.16). 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 severity of dependence, admission for treatment, and level of social support.

Caregiving for addicts is stressful and depends on patient-related problems and caregiver situations and income. Strategies to provide social support, financial aid, and problem-solving skills should be provided to the addicts and their family caregivers as a part of the treatment programs to help to reduce the burden of care and improve their conditions.

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.

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.

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.

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.

Dangerous behavior adversely affects the health of adolescents and young adults. The purpose of this study was to identify subgroups of college students based on the parameters of risky behavior and to analyze the impact of demographic factors and Internet gaming disorder belonging to each class. Study design: A cross sectional study.

The study was conducted in 2020 on 1355 students through a multi-stage random sampling method. A survey questionnaire was used to collect data and all students completed 1294 sets of questionnaires. We analyzed the data using T-test and latent class analysis.

Three latent classes have been identified. a) low-risk (75%), b) tobacco smoker (8%), and c) high-risk (17%). There was a high possibility of risky behavior in the third class. Being single (OR=2.28), Not having a job along with education (OR=1.54), and internet gaming disorder (OR=1.06) increased the risk of inclusion in the tobacco smoker class. Also not having a job along with education (OR=1.43) increased being in the high-risk class.

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

It is of utmost importance to identify populations with the elevated risk for Covid-19 and the factors influencing its outcomes. The present study aimed to investigate the factors affecting mortality and length of stay (LOS) in the hospitals of East Azerbaijan Province, Iran, during a 15 months period of this pandemic. Study design: A retrospective study.

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

In total, 24,293 inpatients with the mean age of 53.99±19.37 years old were included in this study. About 15% of patients lost their lives. The mean age of the deceased patients was 69.02±14.64 years old and significantly higher than the recovered ones (p<0.001). Aging, male gender and having chronic diseases were correlated with the patient mortality. In addition,   aging and having chronic diseases were associated with higher LOS in hospitals.

The older patients were at a higher risk of mortality and even prolonged hospitalization. In addition, patients’ underlying diseases could cause a severe form of COVID-19 and these individuals were more likely to lose their lives and stay in hospitals for a longer time due to COVID-19.

Age at menarche affects women’s health outcomes and could be a risk factor for some diseases such as Metabolic Syndrome. We assessed the association between age at menarche and metabolic syndrome components in women aged 35 to 70 in Hoveyzeh, southwest Iran. Study Design: A case-control study

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

The mean age at menarche was 12.60 ± 1.76 years old. Urban and rural women differed in their age at menarche (12.58±1.71 and 12.63±1.83 years, respectively). A comparison of the four menarche age groups (≤10, 11-12, 13-14, 15-16 years) was statistically different between age at menarche and MetS. The odds of having metabolic syndrome for groups with menarche ages 13-14 years and 16-15 years, compared to women with a menstrual age ≤10 years, decreased by 21% and 20%, respectively.

The present study showed the effect of age at menarche on the odds of having MetS in women 35-70.

Accurate determination of the basic reproduction number (R0) is a very important strategy in the epidemiology of contagious diseases, including COVID-19. This study compares different methods of estimating the R0 of susceptible population, to identify the most accurate method for estimating the R0.

The value of R0 was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time dependent (TD), and sequential Bayesian (SB) methods, for Iran, USA, UK, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. Study design: Cross-sectional study

The lowest MSE was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of R0 based on the TD method, it was found that R0 values in the UK (1.33 (1.14, 1.52)) and the USA (1.25 (1.12, 1.38)) significantly have been more than in other countries.

The important result of this study is that TD and ML methods lead to a more accurate estimate of R0 of population than other methods. Therefore, in order to design accurate and practical vaccination strategies, as well as control COVID-19 and similar diseases using these two methods is suggested to more accurately estimate R0.