The relation between obesity and breast cancer risk in women by considering menstruation status and geographical variations: a systematic review and meta-analysis.

Given the increase in the incidence of breast cancer during the past decades, several studies have investigated the effects of variables on breast cancer, especially obesity. This systematic review and meta-analysis aims to evaluate any effects of obesity on breast cancer risk in women, before and after menopause, and in different continents.All forms of relevant literature examining any association between obesity and breast cancer, including cohort, case-control, and cross-sectional studies, were identified in the PubMed, Scopus, EMBASE, and Web of Science databases from January 1, 1990 until January 13, 2023. Body mass index (BMI) > 30 was used to indicate obesity. Every type of breast cancer was examined as outcome factors. The quality of the papers was evaluated using the Newcastle-Ottawa scale checklist. The Egger and Begg test was used to evaluate publication bias. To assess any extra impact of each research on the final measurement, a sensitivity analysis was carried out.One hundred and two studies were included in this meta-analysis. Respectively, 48 and 67 studies reported associations between obesity and breast cancer in pre and post menopausal women. Combining all studies, the pooled OR of the association between obesity and breast cancer in pre-menopausal women was OR = 0.93 CI: (0.85-1.1), (I2 = 65.4%), and for post-menopausal woman, OR = 1.26 CI: (1.19-1.34), (I2 = 90.5%).Obesity has a protective role in breast cancer among pre-menopausal women, but this relationship is statistically significant only in European women. The chance of developing breast cancer increases in post-menopausal women who are obese. This relationship is significant among Asian, North American, African and European women.

Factors affecting the number of road traffic accidents in Kerman province, southeastern Iran (2015-2021).

Road traffic accidents (RTAs) are among the top causes of mortality and disability globally, particularly in developing nations like Iran. In this study, RTAs were analyzed to develop precise predictive models for predicting the frequency of accidents in the Kerman Province (southeastern Iran) using the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modeling methods. The findings demonstrate that including factors regarding humans, vehicles, and elements of nature in the time-series analysis of accident records resulted in the development of a more reliable prediction model than utilizing only aggregated accident count. The understanding of safety on the road is increased by this research, which also offers a method for forecasting that utilizes a variety of parameters relating to people, cars, and the environment. The findings of this research are likely to contribute to lowering the incidence of RTAs in Iran.

The Relationship Between Population-Level SARS-CoV-2 Cycle Threshold Values and Trend of COVID-19 Infection: Longitudinal Study.

BACKGROUND: The distribution of population-level real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values as a proxy of viral load may be a useful indicator for predicting COVID-19 dynamics. OBJECTIVE: The aim of this study was to determine the relationship between the daily trend of average Ct values and COVID-19 dynamics, calculated as the daily number of hospitalized patients with COVID-19, daily number of new positive tests, daily number of COVID-19 deaths, and number of hospitalized patients with COVID-19 by age. We further sought to determine the lag between these data series. METHODS: The samples included in this study were collected from March 21, 2021, to December 1, 2021. Daily Ct values of all patients who were referred to the Molecular Diagnostic Laboratory of Iran University of Medical Sciences in Tehran, Iran, for RT-PCR tests were recorded. The daily number of positive tests and the number of hospitalized patients by age group were extracted from the COVID-19 patient information registration system in Tehran province, Iran. An autoregressive integrated moving average (ARIMA) model was constructed for the time series of variables. Cross-correlation analysis was then performed to determine the best lag and correlations between the average daily Ct value and other COVID-19 dynamics-related variables. Finally, the best-selected lag of Ct identified through cross-correlation was incorporated as a covariate into the autoregressive integrated moving average with exogenous variables (ARIMAX) model to calculate the coefficients. RESULTS: Daily average Ct values showed a significant negative correlation (23-day time delay) with the daily number of newly hospitalized patients (P=.02), 30-day time delay with the daily number of new positive tests (P=.02), and daily number of COVID-19 deaths (P=.02). The daily average Ct value with a 30-day delay could impact the daily number of positive tests for COVID-19 (ß=-16.87, P<.001) and the daily number of deaths from COVID-19 (ß=-1.52, P=.03). There was a significant association between Ct lag (23 days) and the number of COVID-19 hospitalizations (ß=-24.12, P=.005). Cross-correlation analysis showed significant time delays in the average Ct values and daily hospitalized patients between 18-59 years (23-day time delay, P=.02) and in patients over 60 years old (23-day time delay, P<.001). No statistically significant relation was detected in the number of daily hospitalized patients under 5 years old (9-day time delay, P=.27) and aged 5-17 years (13-day time delay, P=.39). CONCLUSIONS: It is important for surveillance of COVID-19 to find a good indicator that can predict epidemic surges in the community. Our results suggest that the average daily Ct value with a 30-day delay can predict increases in the number of positive confirmed COVID-19 cases, which may be a useful indicator for the health system.

Investigation of risk factors for tunneled hemodialysis catheters dysfunction: competing risk analysis of a tertiary center data.

BACKGROUND: Hemodialysis tunneled catheters are prone to failure due to infection or thrombosis. Prediction of catheter dysfunction chance and finding the predisposing risk factors might help clinicians to prolong proper catheter function. The multidimensional mechanism of failures following infection or thrombosis needs a multivariable and comprehensive analytic approach. METHODS: A longitudinal cross-sectional study was implemented on 1048 patients admitted for the first hemodialysis tunneled catheterization attempt between 2013 and 2019 in Shahid Hasheminejdad hospital, Tehran, Iran. Patients' information was extracted from digital and also paper records. Based on their criteria, single and multiple variable analyses were done separately in patients with catheter dysfunction due to thrombosis and infection. T-test and Chi-square test were performed in quantitative and categorical variables, respectively. Competing risk regression was performed under the assumption of proportionality for infection and thrombosis, and the sub-distributional hazard ratios (SHR) were calculated. All statistical inferences were made with a significance level of 0.05. RESULTS: Four hundred sixty-six patients were enrolled in the analysis based on study criteria. Samples' mean (SD) age was 54(15.54), and 322 (69.1%) patients were female. Three hundred sixty-five catheter dysfunction cases were observed due to thrombosis 123(26.4%) and infection 242(52%). The Median (range) time to catheter dysfunction event was 243(36-1131) days. Single variable analysis showed a statistically significant higher proportion of thrombosis in females (OR = 2.66, 95% CI: 1.77-4.00) and younger patients, respectively. Multivariate competing risk regression showed a statistically significant higher risk of thrombosis in females (Sub-distributional hazard (SHR) = 1.81), hypertensive (SHR = 1.82), and more obese patients (BMI SHR = 1.037). A higher risk of infection was calculated in younger (Age SHR = 0.98) and diabetic (SHR = 1.63) patients using the same method. CONCLUSION: Female and hypertensive patients are considerably at higher risk of catheter thrombosis, whereas diabetes is the most critical risk factor for infectious catheter dysfunction. Competing risk regression analysis showed a comprehensive result in the assessment of risk factors of catheter dysfunction.

Associated factors of first-birth interval among women in reproductive age, addressing maternal and child health.

BACKGROUND: The first-birth interval directly influences family size and maternal and child mortality. The objective of this study is to investigate factors associated with the time of the first-birth after the first marriage among women in Iran. METHODS: In this cross-sectional study, the first birth history of 1350 women aged 15-49 years was collected in Kerman (southern Iran) in 2018. To assess the predictor variables of the first-birth interval and calculate the adjusted hazard ratios, multivariate Cox regression was used. The P-value < 0.05 was considered as significant. The statistical analysis of data was performed using SPSS version 22. RESULTS: The average of the first-birth interval was 2.5 ∓ 0.8 years. Woman's age at marriage (HR 1.48, 95% CI 1.32-2.48), husband's age at marriage (HR 1.88, 95% CI 1.62-2.03), age at the first menstruation (HR 1.53, 95% CI 1.24-2.53), being rural residents (HR 2.041, 95% CI 1.26-2.95), and having engagement period (HR 1.85, 95% CI 1.52-3.05) were associated with short first-birth interval, and woman's BMI (HR 1.72, 95% CI 1.54-2.77), woman's university educational level (HR 1.47, 95% CI 1.35-2.57), husband' s university educational level (HR 1.39, 95% CI 1.32-2.51), contraception use (HR 2.28, 95% CI 1.12-2.86) and income sufficiency (HR 2.25, 95% CI 1.12-2.96) were associated with long first-birth interval. CONCLUSIONS: Being a rural resident is the most influential predictor of short first-birth interval and income sufficiency is the most influential predictor of long first-birth interval.

Total Fertility Rate, which is the average number of live births that would be born to a woman over her reproductive life if she experiences the current age-specific fertility rates throughout her lifetime. The fertility pattern can be measured by several indicators such as the first-birth interval (FBI) after marriage. FBI is defined as the duration of time spent by married couples to have their first child since the first day of marriage. Some studies have demonstrated that the length of FBI subsequently influences spacing and childbearing pattern of a family. The aim of this study is first to determine the mean of FBI and second to explore the associated factors of FBI in Kerman, southern Iran. We analyzed data for 1350 women 15­49 years old. The outcome variable was the first-birth interval (FBI). Results showed that the average of FBI was 2.5 years. There is a positive association between FBI and age of wives at marriage, first menace age, wives' BMI, wives and husbands' educational level, contraception use, age of husbands at marriage, residence place, income sufficiency, and having engagement period. The women and men who got married at older ages were more likely to report short FBI. It was indicated that Educated women and men and women who experience menstruation at a younger age are more likely to report longer FBI. The knowledge about importance of FBI should be more announced for couples by health services in order to help them manage their child-spacing and fertility. The government also should pay attention on FBI as an important index in determining the average age of country population.

Prevalence and Associated Factors of Anxiety and Depression Among Patients with Type 2 Diabetes in Kerman, Southern Iran.

PURPOSE: Depression and anxiety are common disorders in patients suffering from type 2 diabetes. These disorders can lead to premature morbidity, exacerbate disease complications, make patients suffer more, and increase health-care costs. As diabetes has increased worldwide recently, it is necessary to reduce the prevalence of factors that are associated with depression and anxiety in diabetes patients. This study aimed to assess the prevalence of anxiety and depression and to identify their associated factors, including metabolic components among people with type 2 diabetes. PATIENTS AND METHODS: We performed a cross-sectional study in 1500 patients with type 2 diabetes in Kerman, in the southern part of Iran. The prevalence of depression and anxiety was estimated using the Beck Depression Inventory and the Hamilton Anxiety questionnaires, respectively. After calculating the proportions of depression and anxiety, univariate logistic regression was performed. Factors whose P-values were smaller than 0.2 in univariate logistic regression were included in multiple logistic regression for confounder adjustments. The analysis was performed using SPSS version 20. RESULTS: The rates of depression and anxiety were 59% (95% CI: 54.48-63.12) and 62% (95% CI: 59.51-66.27), respectively. Factors found to be independently associated with anxiety were high FBS, high LDL-C, high TG, hypertension, complications, low physical activity. Factors found to be independently associated with depression were female gender, older age, high BMI, high FBS, high LDL-C, low HDL-C, high TG, high HbA1c, hypertension, and low physical activity. Complications were independently associated with anxiety but not with depression. Female gender, older age, high BMI, low HDL-C, and high HbA1c were independently associated with depression but not with anxiety. CONCLUSION: Current findings demonstrated that a large proportion of patients with type 2 diabetes suffer from depression and anxiety. This study also identified factors associated with these disorders. Controlling some metabolic variables will decrease the prevalence of these disorders and improves clinical remedy and quality of life in patients with type 2 diabetes.

Metabolic factors that affect health-related quality of life in type 2 diabetes patients: a multivariate regression analysis.

PURPOSE: In the past few decades, an increase in the life span of the population has caused more people to experience chronic diseases, such as type 2 diabetes. Since chronic diseases influence the whole life of patients and do not have a specific remedy, improving their health-related quality of life (HRQoL) becomes more important. The aim of the present study was to investigate the metabolic variables that influenced HRQoL questionnaire scores most significantly among patients with type 2 diabetes. PATIENTS AND METHODS: The population of this cross-sectional study included 163 patients with type 2 diabetes and 214 healthy people who were asked to complete the HRQoL questionnaire, and their metabolic blood variables were recorded simultaneously. The effects of metabolic variables and some other demographic ones on two main scales of HRQoL, Mental Component Summary (MCS) and Physical Component Summary (PCS), were evaluated using multivariate regression. RESULTS: Multivariate regression analysis showed that PCS score was most negatively influenced by cigarette smoking (P=0.009, ß-15.761), maximum blood pressure (P=0.008, ß=-0.108), minimum blood pressure (P=0.009, ß=-0.039), low-density lipoprotein cholesterol (P=0.008, ß=-0.721), cholesterol (P=0.006, ß=-0.648), HbA1c (P=0.004, ß=-0.878), FBS (p=0.006, ß=-0.769), and body mass index (BMI) (P=0.034, ß=-0.287). The MCS score was influenced positively by smoking cigarettes (P=0.041, ß=13.032), gender (P=0.018, ß=15.633), and BMI (P=0.048, ß=-0.088). Men had a higher MCS score compared to women. CONCLUSION: The HRQoL questionnaire (as a concept of health) score could be improved by controlling the variation of some metabolic variables in patients with type 2 diabetes. Some metabolic variables could be the main causes of a decrease in physical and mental HRQoL among patients with type 2 diabetes.