Journal of Biostatistics and Epidemiology

Iran’s Hypertension and Diabetes Mellitus Surveillance System and Its Implementation Challenges: A Case Study of Tehran Uni- versity of Medical Sciences

2025-12-20T10:46:07+00:00

Introduction: Surveillance systems play a vital role in managing non-communicable diseases (NCDs). This study explores thechallenges encountered in the implementation of Iran's hypertension (HTN) and diabetes surveillance system, using TehranUniversity of Medical Sciences (TUMS) as a case study.

Methods: This study employed a two-part approach, consisting of a literature review and an expert panel discussion. In the firstpart, a literature review was conducted using PubMed and Google Scholar, encompassing studies published between 2000 and2023 in both Persian and English. The objectives of this review were to: 1) investigate the history of diabetes and HTN surveillancesystem and 2) identify implementation challenges of the surveillance system for these conditions. The sources included nationalguidelines, health surveys, published reports, and academic papers. In the second part, five expert panel discussions (formalfocus groups) were conducted semi-structured with seven TUMS specialists. These sessions aimed to pinpoint the challengesin implementing diabetes and HTN surveillance in Iran. Finally, data from both parts were analyzed using conventional contentanalysis with an inductive approach and were categorized and coded using MAXQDA software to extract the key challenges inimplementing the surveillance system.

Results: The National HTN Prevention and Control Program was initiated in 1992 and later expanded its focus to includeindividuals (both males and females) aged 30 and older in rural areas. In 2004, it merged with the National diabetes Preventionand Control Program, which was established in 1991. This program primarily targeted high-risk individuals aged 15 to 39, as wellas those over 40 in selected pilot rural areas. Together, these programs now address both HTN and diabetes prevention throughthe IraPEN program, launched in 2014. The analysis identified three main categories and eleven subcategories, resulting in a totalof 78 codes. The issues have been categorized into three areas: healthcare recipients (lack of public awareness and social issues),healthcare system employees (staffing issues and inadequate training), and upper levels of the healthcare system (ineffectivepolicies, poor evaluation and monitoring, outdated facilities, weak management, insufficient data, and financial constraints).

Conclusion: The study identifies challenges within Iran's healthcare system that impede surveillance programs. To improveoutcomes, we should prioritize public education, provide better support for healthcare workers, and implement strongermanagement practices. By adopting these changes, we can enhance the surveillance systems for HTN and diabetes.

Exploring the Incidence and Time to Onset of Side Effects Associated with COVID-19 Vaccine

2025-12-20T10:46:06+00:00

Introduction: Close monitoring of side effects and their time-to-onset following COVID-19 vaccination is essential toensure vaccine safety and maintain public confidence. The study aims to find the incidence, time of side effects, and factorsresponsible for the COVID-19 vaccine.

Methods: A cross-sectional design was used to collect data from 716 recipients of the first COVID-19 vaccine dose at SikkimManipal University, Sikkim, using a validated, self-administered questionnaire. Survival analysis was applied to find theincidence, time, and causes of side effects.

Results: Of the participants, 79.5% experienced at least one side effect, with pain at the vaccination site (73.8%) and fever(65.2%) being the most frequently reported. Maximum side effects occurred within 12 hours of vaccination. Among thesurvival models evaluated, the log-logistic model demonstrated the best fit for characterizing the time to symptom onset.The analysis revealed that age (Hazard Ratio [HR] = 1.51), education level (HR = 2.28), and use of prior medication (HR =1.48) were significant risk factors for post-vaccination side effects.

Conclusion: This study highlights the importance of early monitoring of side effects following COVID-19 vaccination toensure vaccine safety and maintain public confidence. The finding indicated that most side effects occurred within 12hours of vaccination. Among the models assessed, the log-logistic survival model demonstrated the best fit for modeling thetime to onset of vaccine side effects. Older age, lower education, and use of prior medication were identified as significantpredictors of the post-vaccination side effects. These findings support the early monitoring and personalized care to ensurevaccine safety, health, and sustain public confidence.

Determinants of Family Planning Service Utilization among Palestine Refugees at UNRWA Health Center in Amman, Jordan: A Qualitative and Quantitative Mixed-Methods Study

2025-12-20T10:46:05+00:00

Introduction: Family planning is a vital aspect of reproductive health, encompassing contraceptive use, pregnancy, and prevention of sexually transmitted infections (STI). Among Palestine refugees in Jordan, particularly those utilizing services by The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), maternal mortality rates and contraceptive use highlight the urgent need to address gaps in family planning service utilization. Sociocultural factors and systemic barriers remain determinants of contraceptive use and family planning outcomes in this population.

Methods: A mixed-methods study was conducted from June 5–7, 2023, at a single UNRWA health center in Marka camp located in Amman, Jordan. The study involved structured interviews with 57 female participants, focus group discussions (FGDs) among participants, and semi-structured interviews with healthcare providers at Marka health center. Quantitative data collection encompassed sociodemographic factors, perceptions of family planning services, and sociocultural determinants influencing contraceptive use. Categorical variable findings were analyzed by frequency and proportions. Subsequently, an exploratory descriptive approach was taken to gather qualitative data via FGD and semi-structured interviews. Transcripts from FGDs and interviews were coded and synthesized by thematic analysis to identify salient determinants of family planning knowledge, perceived barriers and self-efficacy to find access to family planning services.

Results: Major findings reported include: (1) Internet and health care professionals are cited as primary sources of knowledge regarding family planning with the most common reason for choosing the preferred contraception being professional advice (22.2%), (2) Sociocultural factors significantly shaped decisions, with a majority of FGD participants citing spousal preferences as the key determinant, and (3) Perceptions of UNRWA’s services were largely positive, with 98.2% rating them excellent or good. However, logistical challenges such as long wait times and transportation costs were frequently reported as barriers. These challenges did not seem to diminish the overall satisfaction for patients, but did hinder utilization of services.

Conclusion: While UNRWA’s family planning services are well-regarded, persistent barriers such as sociocultural constraints, limited knowledge, and service accessibility require targeted interventions. Fostering supportive sociocultural environments and improving logistical factors like wait times can enhance service uptake. Future research should explore long-term impacts of family planning initiatives and expand the scope to include other UNRWA health centers to inform inclusive and effective health policies pertaining to reproductive health in this low-income setting.

A Multifunctional Approach to Feature Extraction from fMRI Im- ages in Alzheimer's disease

2025-12-20T10:46:04+00:00

Introduction: The use of fMRI imaging in medical science has led to the diagnosis of diseases at the very first stages before the disease get advancedwhich plays a significant role in some diseases such as Alzheimer's. Extracting useful information from these images is the first step in the initial diagnosis of the disease that the accuracy in extracting as much of this information as possible contributes significantly to the initial diagnosis. Increases the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used.

Methods: The information of 140 patients with Alzheimer's disease was obtained, and the stable multipurpose feature extraction method was used to extract the information. In this way, two-level wavelet, modeling of wavelet coefficients, normalization method and feature selection are applied.

Results: The results obtained from the examination of 285 features in five categories showed that some of the information contained in the features overlapped and lacked useful information. In addition, dimensionality and noise reduction using the PCA algorithm showed that about 41% of the relevant features are outliers or missing information.

Conclusion: In general, increasing the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used.

Integration of Time into a 2-Dimensional Geography to Visualize Spatio-Temporal Clusters of Dog Rabies in Thailan

2025-12-20T10:46:03+00:00

Introduction: Space-time scanning analysis to detect cylindrical spatio-temporal clusters of diseases is available. Yet, thereis no satisfactory way to visualize the data. Our aim is to visualize spatio-temporal cylindrical clusters of dog rabies inThailand from 2005 to 2021.

Methods: We obtained dog rabies data from 2005 to 2021 from the World Animal Health Information System under theWorld Organisation for Animal Health (WOAH). The rsatscan package in R was applied to identify spatio-temporal clustersof dog rabies using the discrete Poisson model and Monte Carlo simulation. Using a user-defined function developed by ourresearch team, cylindrical shapes were created based on the provincial administration maps to demonstrate significantclusters over space and time.

Results: The average incidence of dog rabies was 0.5 events per 100,000 human-years, and seven clusters were foundduring the study period in all five national regions, based on 15% of the population being at risk.

Conclusion: Our method to generate multi-dimensional graphics can comprehensibly visualize cylinder-shaped outcomesfrom spatio-temporal data. With the relatively large number of dog rabies clusters detected, more intensive controlmeasures are required to alleviate dog rabies

Classifying Substance Abuse Tendencies Using the Naive Bayes Algorithm

2025-12-20T10:46:02+00:00

Introduction: Uncertainty in human life often arises from a lack of knowledge based on past events or unrealized circumstances. The Naive Bayes classification technique, rooted in conditional probability, offers a hypothesis-driven approach to linking two random occurrences and calculating posterior probabilities. Substance addiction remains a critical issue, particularly in patients hospitalized in community mental health centers, necessitating effective predictive methods for early identification and intervention.

Methods: This study employed the Naive Bayes algorithm to classify substance addiction tendencies in patients. Data of all 205 patients registered at the Giresun Province Prof. Dr. A. Ilhan Ö�zdemir State Hospital Community Mental Health Center was obtained from the database. To enhance prediction accuracy, feature selection was conducted using the Information Value (IV) method. Ten patient attributes were analyzed, including gender, education level, marital status, income status, urban status, living alone, family disease, relation with family and environment, activity status, and age. Features with strong or medium predictive power were prioritized for the model. Accuracy, recall, precision, and F1 score were used as evaluation metrics of the model.

Results: Based on the strong or medium predictive power of IV, four features: gender, education level, income status, and relationship status with family and environment (respectively 0.45, 0.2, 0.17, and 0.17) were related to substance abuse. The Naive Bayes algorithm revealed that males (78%) are approximately four times more likely than females (22%) to develop substance addiction. Patients with education levels ranging from primary to high school were more prone than those with college-level education or higher. Additionally, those under state protection exhibited a higher likelihood (39%) of substance abuse compared to other income statuses. Finally, individuals with poor or neutral relationships with family and their environment were more susceptible to addiction (30%). Respectively, recall, precision, F1 score, and accuracy were obtained as 75%, 65%, 70%, and 76%, indicating the proper classification rate.

Conclusion: The Naive Bayes algorithm effectively classified substance addiction tendencies in hospitalized patients, emphasizing key predictive factors such as gender, education level, income status, and relational dynamics. These findings highlight the importance of targeted interventions tailored to at-risk populations, improving early detection and management strategies in community mental health settings

A Heuristic Model of Primary Brain Cancers and Serious Mental Illnesses, etc., Supporting the Idea of Different Clinical Expres- sions with Aging of the Same Epigenetic Factors

2025-12-20T10:46:01+00:00

Introduction: Combined epidemiological hospital data on primary brain diagnoses; primary brain cancers and serious mental illnesses, etc., of the age on first admission are suggestive of common epigenetic factors, among others. We propose a heuristic model combining age distributions of both entities which show patterns of a composed normal distribution supporting the idea of different clinical expressions with aging of the same epigenetic factors.

Methods: Retrospective data from Electronic Patients Records (EPR), TUHC “Mother Teresa”, Albania, years 2005-2021, were analyzed after creating cutoffs on age, county (capital), first admission and diagnosis groups. Combination of age at first hospitalization for the main groups of diagnoses was tested for normality, hypothesized to be a composed normal distribution.

Results: The total number of patients for the period 2005-2021, was 48,303 admissions. First admissions were N=31,603 (65.4%), of which N=15,896 (50.3%) were from Tirana (the capital) county. The number of first admissions for the two main categories ‘Mental Disorders’ and ‘Other’ for Tirana (the capital) county were respectively; 6,807 (42.8%) and 9,089 (57.2%). Age on first admission mean (median) for Tirana county was 35.1 ± 22.1 (36.0) years. Age on first admission, mean (median), for the two categories for Tirana county were respectively; 37.7 ± 15.4 (37.2) years and 33.0 ± 26.3 (31.0) years and split by sex; male (36.2 ± 15.4 (35.3) years and 32.4 ± 26.4 (28.0) years, p<0.001) and female (39.4 ± 15.4 (39.6) years and 33.8 ± 33.0 (26.0) years, p<0.019). Age-specific distribution (> 15years) data on ‘Mental Disorders’, ‘Other’ and ‘Total’ for Tirana county test positive about normality, Shapiro-Wilk > 0.005 but data for ‘mental disorders’ don’t pass the ‘‘95% range check’’.

Conclusion: Independent and common factors of brain cancers and serious mental illnesses, etc., show patterns of a composed normal distribution at the age of first hospitalization