Cesarean delivery in Iran: a population-based analysis using the Robson classification system

BACKGROUND

The rise of Cesarean Sections (CS) is a global concern. In Iran, the rate of CS increased from 40.7% in 2005 to 53% in 2014. This figure is even higher in the private sector.

OBJECTIVE

To analyze the CS rates in the last 2 years using the Robson Classification System in Iran.

METHODS

A retrospective analysis of all in-hospital electronically recorded deliveries in Iran was conducted using the Robson classification. Comparisons were made in terms of the type of hospital, CS rate, and obstetric population, and contributions of each group to the overall cesarean deliveries were reported.

RESULTS

Two million three hundred twenty-two thousand five hundred women gave birth, 53.6% delivered through CS. Robson group 5 was the largest contributing group to the overall number of cesarean deliveries (47.1%) at a CS rate of 98.4%. Group 2 and 1 ranked the second and third largest contributing groups to overall CSs (20.6 and 10.8%, respectively). The latter groups had CS rates much higher than the WHO recommendation of 67.2 and 33.1%, respectively. "Fetal Distress" and "Undefined Indications" were the most common reasons for cesarean deliveries at CS rates of 13.6 and 13.4%, respectively. There was a significant variation in CS rate among the three types of hospitals for Robson groups 1, 2, 3, 4, and 10.

CONCLUSION

The study revealed significant variations in CS rate by hospital peer-group, especially for the private maternity units, suggesting the need for further attention and audit of the Robson groups that significantly influence the overall CS rate. The study results will help policymakers identify effective strategies to reduce the CS rate in Iran, providing appropriate benchmarking to compare obstetric care with other countries that have better maternal and perinatal outcomes.

The visceral adiposity index and lipid accumulation product as predictors of cardiovascular events in normal weight subjects

INTRODUCTION

Visceral adipose tissue (VAT) has an important role in the incidence of cardiovascular disease (CVD) than obesity by itself. The visceral adiposity index (VAI) and lipid accumulation product (LAP) are surrogate indices for measuring VAT. The aimed of this study was to investigate the association of these markers with cardiovascular events among populations with different BMI category in Mashhad, northeast of Iran.

METHOD

The present study comprised a prospective cohort of 9685 men and women (35-65 years) who were recruited from MASHAD study. BMI category was defined as normal weight (BMI <25), over weight (25 ≤ BMI<30) and obese (BMI≥30). Demographic, laboratory evaluations, anthropometric and metabolic parameters were performed. Logistic and Cox regression analyses were used to determine the association and risk of cardiovascular events with VAT and LAP.

RESULTS

The mean VAI and LAP in CVD patients were significantly higher than in healthy ones in all 3 groups. In terms of CVD event prediction, VAI and LAP had significant association with the incidence of CVD in the second (RR (95% CI): 2.132 (1.047-4.342) and 2.701 (1.397-5.222), respectively) and third tertiles (RR (95% CI): 2.541 (1.163-5.556) and 2.720 (1.159-6.386), respectively) in the normal group, but this association was only found in the third tertiles (RR (95% CI): 2.448 (1.205-4.971) and 2.376 (1.086-5.199), respectively) in the overweight group. The result couldn't find this association for the obese group.

CONCLUSION

In this study, we found that there was a significant association between LAP and VAI and cardiovascular events in normal weight and over-weight groups; however, no significant relationship was found in the obese group.

Factors Associated With Trabecular Bone Score and Bone Mineral Density; A Machine Learning Approach

INTRODUCTION

Bone indexes including trabecular bone score (TBS) and bone mineral density (BMD) have been shown to be associated with wide spectrum of variables including physical activity, vitamin D, liver enzymes, biochemical measurements, mental and sleep disorders, and quality of life. Here we aimed to determine the most important factors related to TBS and BMD in SUVINA dataset.

METHODS

Data were extracted from the Survey of Ultraviolet Intake by Nutritional Approach (SUVINA study) including all 306 subjects entered this survey. All the available parameters in the SUVINA database were included the analysis. XGBoost modeler software was used to define the most important features associated with bone indexes including TBS and BMD in various sites.

RESULTS

Applying XGBoost modeling for 4 bone indexes indicated that this algorithm could identify the most important variables in relation to bone indexes with an accuracy of 92%, 93%, 90% and 90% respectively for TBS T-score, lumbar Z-score, neck of femur Z-score and Radius Z-score. Serum vitamin D, pro-oxidant-oxidant balance (PAB) and physical activity level (PAL) were the most important factors related to bone indices in different sites of the body.

CONCLUSIONS

Our findings indicated that XGBoost could identify the most important variables with an accuracy of &gt;90% for TBS and BMD. The most important features associated with bone indexes were serum vitamin D, PAB and PAL.

A large population-based study on the prevalence of electrocardiographic abnormalities: A result of Mashhad stroke and heart atherosclerotic disorder cohort study

BACKGROUND

Twelve-lead electrocardiogram (ECG) is a common and inexpensive tool for the diagnostic workup of patients with suspected cardiovascular disease, both in clinical and epidemiological settings. The present study was designed to evaluate ECG abnormalities in Mashhad population.

METHODS

ECGs were taken as part of MASHAD cohort study (phase1) and were coded according to the Minnesota coding criteria. Data were analyzed using SPSS.

RESULTS

Total 9035 ECGs were available for final analysis including 3615 (40.0%) male and 5420 (60.0%) female. Among ECG abnormalities precordial Q wave, major T-wave abnormalities, inferior Q wave, sinus bradycardia, and left axis deviation were the most prevalent abnormalities. The frequency of precordial and inferior Q wave, inferior QS pattern, major and minor ST abnormalities, major and minor T abnormalities, Wolff-Parkinson-White and Brugada pattern, sinus bradycardia, sinus tachycardia, left axis deviation, ST elevation, and tall T wave were significantly different between two genders. Moreover, the frequency of Q wave in precordial and aVL leads, QS pattern in precordial and inferior leads, major and minor T-wave abnormalities, Wolff-Parkinson-White, atrial fibrillation, sinus bradycardia, left axis deviation, and ST elevation were significantly different in different age groups. A comparison of the heart rate, P-wave duration, and QRS duration between men and women indicated that there was a significant difference.

CONCLUSIONS

Our finding indicated that the prevalence ECG abnormalities are different between men and women and also it varied in different age groups.

Laboratory Biochemical and Hematological Parameters: Early Predictive Biomarkers for Diagnosing Hepatitis C Virus Infection

BACKGROUND

Hepatitis C virus (HCV) infection is a worldwide concern, causing liver damage and necessitating early detection to prevent its spread. Studies indicate that evaluating changes in biochemical and hematological parameters, which serve as suitable predictors of inflammation, can be a reasonable method for diagnosing hepatitis C infection.

METHODS

This study analyzed 100 samples from high-risk patients positively identified via quantitative real-time PCR (qPCR). Anti-HCV titers, biochemical and inflammatory tests, and complete blood cell counts (CBCs) were performed for these individuals. Additionally, 100 HCV-negative individuals with normal laboratory results were selected as the control group. Receiver operating characteristic (ROC) curves were plotted to determine the cutoff values of the laboratory parameters.

RESULTS

According to the findings, the age, average white blood cell (WBC) count, platelet-to-lymphocyte ratio (PLR), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), lactate dehydrogenase (LDH), total bilirubin (TBIL), direct bilirubin (DBIL), alkaline phosphatase (ALP), serum glutamic-pyruvic transaminase (SGPT), and ferritin levels were significantly higher in HCV patients. On the other hand, red blood cell (RBC) counts, neutrophils, lymphocytes, hemoglobin-to-platelet ratio (HPR), and iron (Fe) levels were significantly lower in the case group compared to those in the control group (p < 0.05). Furthermore, the ROC curve analysis revealed that lymphocyte count, neutrophil count, and PLR were very strong predictors for hepatitis C infection (p < 0.0001, AUC = 1).

CONCLUSION

The study highlights significant biochemical and hematological differences between HCV patients and healthy subjects. These biomarkers are crucial for early diagnosis, potentially preventing liver damage and reducing HCV transmission.

Using meta-learning to recommend an appropriate time-series forecasting model

BACKGROUND

There are various forecasting algorithms available for univariate time series, ranging from simple to sophisticated and computational. In practice, selecting the most appropriate algorithm can be difficult, because there are too many algorithms. Although expert knowledge is required to make an informed decision, sometimes it is not feasible due to the lack of such resources as time, money, and manpower.

METHODS

In this study, we used coronavirus disease 2019 (COVID-19) data, including the absolute numbers of confirmed, death and recovered cases per day in 187 countries from February 20, 2020, to May 25, 2021. Two popular forecasting models, including Auto-Regressive Integrated Moving Average (ARIMA) and exponential smoothing state-space model with Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS) were used to forecast the data. Moreover, the data were evaluated by the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) criteria to label time series. The various characteristics of each time series based on the univariate time series structure were extracted as meta-features. After that, three machine-learning classification algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used as meta-learners to recommend an appropriate forecasting model.

RESULTS

The finding of the study showed that the DT model had a better performance in the classification of time series. The accuracy of DT in the training and testing phases was 87.50% and 82.50%, respectively. The sensitivity of the DT algorithm in the training phase was 86.58% and its specificity was 88.46%. Moreover, the sensitivity and specificity of the DT algorithm in the testing phase were 73.33% and 88%, respectively.

CONCLUSION

In general, the meta-learning approach was able to predict the appropriate forecasting model (ARIMA and TBATS) based on some time series features. Considering some characteristics of the desired COVID-19 time series, the ARIMA or TBATS forecasting model might be recommended to forecast the death, confirmed, and recovered trend cases of COVID-19 by the DT model.

Adherence to the nordic diet is associated with anxiety, stress, and depression in recovered COVID-19 patients, a case-control study

BACKGROUND

Follow-up of COVID-19 recovered patients to discover important adverse effects on other organs is required. The psychological health of COVID-19 patients may be affected after recovery.

AIM

We aimed to evaluate the association between adherence to the Nordic diet (ND) and psychological symptoms caused by COVID-19 after recovery.

METHOD

Dietary data on 246 qualified adults (123 cases and 123 controls). The dietary intake in this case-control study was calculated by a reliable and valid food frequency questionnaire (FFQ). Depression Anxiety Stress Scale (DASS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Short-Form Health Survey (SF-36) were used to analyze participant's anxiety, stress, depression, sleep quality, insomnia, and quality of life of participants.

RESULTS

There was a significant inverse relationship between total anxiety, stress, and depression scores and the intake of whole grains (P < 0.05). Furthermore, there was a significant inverse association between depression and fruit intake (P < 0.05). A significant negative correlation was found between insomnia and sleep quality and the intake of root vegetables (P < 0.05). In the multinomial-regression model, a significant association between the Nordic diet and anxiety, stress, and depression was found only in the case group (OR = 0.719, 95% CI 0.563-0.918, p-value = 0.008; OR = 0.755, 95% CI 0.609-0.934, P-value = 0.010, and, OR = 0.759, 95% CI 0.602-0.956, P-value = 0.019 respectively).

CONCLUSION

Adherence to the Nordic diet might reduce anxiety, stress, and depression in recovered COVID-19 patients.

Investigating the relationship between self-efficacy and quality of life in Iranian women

BACKGROUND

Women's quality of life and self-efficacy play pivotal roles in task accomplishment and overall health improvement within families and society. This study determination the intricate relationship between quality of life and self-efficacy among women utilizing care services from Mashhad health centers.

METHODS

A cross-sectional study involving 366 women accessing Mashhad health centers in 2023 was conducted. Clustering sampling was employed, and data were gathered using the Schwartz self-efficacy questionnaire and the short form of quality of life. Statistical analysis utilized Spearman correlation coefficient, Kruskal-Wallis test, Mann-Whitney U-test, Wilcoxon test, and Chi-square test in SPSS25, with a significance level set at 0.05.

RESULTS

Participants' mean age was 36.42 ± 11.13 years. A statistically significant relationship was observed between self-efficacy and total quality of life score, as well as its dimensions (physical health, psychological health, social relationships, social environment and quality of life, and general health) (P < 0.001).

CONCLUSION

The study underscores a significant association between self-efficacy and both the overall quality of life and its specific dimensions among women. These findings highlight the reciprocal influence of self-efficacy and quality of life. Consequently, tailored interventions aimed at enhancing self-efficacy and quality of life are recommended.

Urban and rural residence: their influence on food group consumption in Iran

BACKGROUND

Urbanization is expanding in Iran, leading to the emergence of three distinct socio-geographical areas: urban, rural, and suburban areas. These different areas may exhibit significant variations in dietary patterns. This study investigates the association between people's place of residence and their consumption of different food groups.

METHODS

This study utilized data from Iran's Food and Nutrition Surveillance System (FNS). A total of 1697 participants were randomly recruited from different rural (N = 568), urban (N = 568), and suburban (N = 561) regions across Iran. Their food intake was assessed using a validated dish-based semi-quantitative food frequency questionnaire (DB-FFQ).

RESULTS

Rural males consumed significantly more grains (35.51 g/day, p = 0.03) than urban males, while rural females consumed significantly less dairy (-30.07 g/day, p = 0.03) than urban females. Additionally, rural males and females consumed significantly more fats and oils (3.72 g/day, p = 0.01 for males and 5.2 g/day, p < 0.001 for females) than their urban counterparts. Moreover, both suburban females and males were found to consume significantly less fruit compared to urban individuals, with suburban females consuming - 47.41 g/day (p < 0.001) less fruit and suburban males consuming - 60.42 g/day (p = 0.001) less fruit.

CONCLUSION

Findings showed that rural men's diets are characterized by higher consumption of grains and fats, while urbanization is linked to increased dairy consumption in women. Additionally, the study highlights a worrying lack of fruit consumption in suburban areas. These findings underscore the necessity of implementing specific nutritional policies to promote dietary diversity in various residential areas.

Clustering of the causes of death in Northeast Iran: a mixed growth modeling

BACKGROUND

Processing and analyzing data related to the causes of mortality can help to clarify and monitor the health status, determine priorities, needs, deficiencies, and developments in the health sector in research and implementation areas. In some cases, the statistical population consists of invisible sub-communities, each with a pattern of different trends over time. In such cases, Latent Growth Mixture Models (LGMM) can be used. This article clusters the causes of individual deaths between 2015 and 2019 in Northeast Iran based on LGMM.

METHOD

This ecological longitudinal study examined all five-year mortality in Northeast Iran from 2015 to 2019. Causes of mortality were extracted from the national death registration system based on the ICD-10 classification. Individuals' causes of death were categorized based on LGMM, and similar patterns were placed in one category.

RESULTS

Out of the total 146,100 deaths, ischemic heart disease (21,328), malignant neoplasms (17,613), cerebrovascular diseases (11,924), and hypertension (10,671) were the four leading causes of death. According to statistical indicators, the model with three classes was the best-fit model, which also had an appropriate interpretation. In the first class, which was also the largest class, the pattern of changes in mortality due to diseases was constant (n = 98, 87.50%). Second-class diseases had a slightly upward trend (n = 10, 8.92%), and third-class diseases had a completely upward trend (n = 4, 3.57%).

CONCLUSIONS

Identifying the rising trends of diseases leading to death using LGMM can be a suitable tool for the prevention and management of diseases by managers and health policy. Some chronic diseases are increasing up to 2019, which can serve as a warning for health policymakers in society.

Cq values as an indicator for COVID-19 outcomes: A study of the correlation between laboratory parameters

OBJECTIVE

The ongoing outbreak of the respiratory disease coronavirus disease 2019 (COVID-19) is currently presenting a major global health threat. This pandemic is unprecedented in recent human history. The objective of this study was to examine the relationship between cycle quantitation (Cq) and laboratory parameters in COVID-19 patients, aiming to determine if Cq levels can provide valuable insights into the COVID-19 disease.

METHODS

This study involved 234 participants who were divided into case and control groups. Real-time PCR tests were used to diagnose COVID-19 cases in the study participants. Blood tests, including complete blood count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), D-dimer, IgG, and IgM, were also conducted. Statistical analysis was performed using SPSS 22 software.

RESULTS

The findings showed that COVID-19-positive cases had significantly higher levels of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), D-dimer, ESR, CRP, and LDH compared to normal cases. Additionally, the case group had significantly lower lymphocyte and platelet counts. There was a statistically significant positive correlation between Cq levels and lymphocyte count (r = .124, p = .014). Conversely, there was a statistically significant inverse correlation between Cq levels and NLR (r = -.208, p = .017). Furthermore, the evaluation of hematological, inflammatory, and biochemical indexes in COVID-19 patients using the receiver-operating characteristics curve demonstrated statistically appropriate sensitivity and specificity.

CONCLUSION

Our outcomes indicated a significant association between Cq levels and PLR, NLR, D-dimer, CRP, and ESR in COVID-19 patients. Consequently, including the report of laboratory parameters alongside Cq values offers a promising prognosis.

Investigating the association of acute kidney injury (AKI) with COVID-19 mortality using data-mining scheme

COVID-19 has caused significant challenges in kidney research and disease management. Data mining techniques such as logistic regression (LR) and decision tree (DT) were used to model data. All analyses were performed using SPSS 25 and Python 3. The incidence of acute kidney injury (AKI) was 14.1% and the overall mortality risk was 13% among COVID-19 patients. The mortality was associated with, AKI, age, marital status, smoking status, heart failure, chronic obstructive pulmonary disease, malignancy, and SPO2 level using LR. The accuracy, sensitivity, specificity, and area under the curve of the DT (and LR) classifier were 70% (85%), 73% (75%), 78% (79%), and 77% (81%), respectively. Based on the DT model, the variable most significantly associated with COVID-19 mortality was AKI followed by age, high WBC count, BMI, and lymphocyte count. It was concluded that the incidence of AKI was high, and AKI was identified as one of the important factors that played an effective role in mortality due to COVID-19.

Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique

Previous studies have proposed that heat shock proteins 27 (HSP27) and its anti-HSP27 antibody titers may play a crucial role in several diseases including cardiovascular disease. However, available studies has been used simple analytical methods. This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and direction of the predictors using PFI and SHAP methods. The study employed Python 3 to apply various machine learning models, including LightGBM, CatBoost, XGBoost, AdaBoost, SVR, MLP, and MLR. The best models were selected using model evaluation metrics during the K-Fold cross-validation strategy. The LightGBM model (with RMSE: 0.1900 ± 0.0124; MAE: 0.1471 ± 0.0044; MAPE: 0.8027 ± 0.064 as the mean ± sd) and the SHAP method revealed that several factors, including pro-oxidant-antioxidant balance (PAB), physical activity level (PAL), platelet distribution width, mid-upper arm circumference, systolic blood pressure, age, red cell distribution width, waist-to-hip ratio, neutrophils to lymphocytes ratio, platelet count, serum glucose, serum cholesterol, red blood cells were associated with anti-HSP27, respectively. The study found that PAB and PAL were strongly associated with serum anti-HSP27 antibody titers, indicating a direct and indirect relationship, respectively. These findings can help improve our understanding of the factors that determine anti-HSP27 antibody titers and their potential role in disease development.