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

2026-02-24T22:03:22+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inadequate training), and upper levels of the healthcare system (ineffectivepolicies, poor evaluation and monitoring, outdated facilities, weak management, insufficient data, and financial constraints).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.

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

2026-02-24T22:03:21+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 Thailand

2026-02-24T22:03:20+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

2026-02-24T22:03:19+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

2026-02-24T22:03:18+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

Epidemiology of Parasitic Contamination in Ship Wastewater: A Cross-Sectional Study in Black Sea Ports

2026-02-24T22:03:17+00:00

Introduction: The impact of seawater on freshwater systems is well known. However, its role in the transmission of human diseases has not been sufficiently studied. Marine vessels entering tropical countries annually discharge thousands of tons of wastewater into water bodies. Although most vessels are equipped with wastewater treatment plants (WWTPs), the lack of regulations governing parasitological control creates significant risks of contamination of water bodies with pathogens causing parasitic diseases.

Methods: Between 2006 and 2011, 489 wastewater samples from WWTP-treated vessels arriving at Black Sea ports in Ukraine from parasitic disease-endemic tropical regions were collected. The samples were analyzed for the presence of tropical helminths and for compliance with the “State Sanitary Rules and Norms for the Discharge of Waste, Oil, Ballast Water and Garbage from Ships into Water Bodies” (July 9, 1997, No. 199). Sampling was conducted in accordance with the guidelines of the U.S. Environmental Protection Agency (U.S. EPA Guidance for Sampling and Analysis of Sludge for POTW Facilities, EPA/833/B-89/100). Wastewater analysis was carried out according to the "Standard Methods for the Examination of Water and Wastewater" (APHA, 1995), ecological standards, and technologies of the U.S. EPA — Control of Pathogens and Vector Attraction in Sewage Sludge, as well as the U.S. EPA guidance on the sampling and analysis of POTW sludge.

Results: The study results showed that 36.2% (95% CI: 36.1%–36.3%) of the wastewater samples did not meet bacteriological standards, 39.9% (95% CI: 39.8%–40.0%) did not meet chemical standards, and 32.5% (95% CI: 32.4%– 32.6%) of the wastewater samples were contaminated with parasite eggs and cysts.

Conclusion: It has been demonstrated for the first time that the WWTPs of marine vessels arriving from tropical regions, which do not ensure the deworming of wastewater, pose a potential health risk to populations living in coastal areas

Optimizing Maternal Health in Refugee Settings: Perspectives on the WHO's Enhanced Antenatal Care Schedule: A Mixed- Methods Study in Zarqa, Jordan

2026-02-24T22:03:16+00:00

Introduction: Antenatal care (ANC) is essential for improving maternal and newborn health by enabling early detectionand treatment of potential complications. In 2016, the World Health Organization (WHO) increased the recommendednumber of ANC visits from four to eight, aiming to enhance maternal health outcomes. This study explores the experiencesof pregnant women in Jordanian refugee camps and examines the perceived impact of the revised WHO ANC schedule.

Methods: A mixed-methods study was conducted at Zarqa health centers in Jordan from May 28, 2023, to July 26, 2023.Data were collected through structured interviews with 46 female patients (pregnant, postpartum, or trying to conceive),focus group discussions (FGDs) with six participants, and semi-structured interviews with six healthcare providers.Quantitative data were gathered using structured questionnaires, while qualitative data were obtained through FGDs andprovider interviews.

Results: Participants unanimously agreed that eight ANC visits are essential for optimal maternal and fetal health.Approximately 90% expressed a need for clearer communication from healthcare providers during appointments. Despitedemonstrating strong self-awareness about when to seek medical attention, participants highlighted key barriers toANC access, including transportation challenges, childcare responsibilities, and long waiting times. Healthcare providersacknowledged these barriers and emphasized the need for improved patient communication and resource allocation.Overall, participants reported general satisfaction with the services provided at refugee health clinics.

Conclusion: Optimizing ANC access in refugee settings requires a multifaceted approach that addresses communicationgaps, logistical challenges, and systemic healthcare barriers to ensure equitable maternal health outcomes