Epidemiology, Biostatistics, and Public Health

Analysis of COVID-19 Pandemic Waves in Death Figures of Turkey

2023-09-25T14:59:51+00:00

Introduction: The COVID-19 pandemic has resulted in a substantial number of deaths worldwide, making it crucial to conduct a periodical behavior analysis of the COVID-19 death figures. By understanding the trends and patterns in mortality rates, decision-makers can make informed choices on prioritization of interventions to pandemics.
Method: Death figures due to COVID-19 between 17 March 2020 and 27 November 2022 were taken into account in this study. Mortality data were analyzed using statistical and graphical methods. Von Bertalanffy’s growth model parameters were estimated for each wave. Then the model performance measures were calculated.
Discussion: In this study, the course and change of daily mortality data over time were examined. The data reveal that the COVID-19 pandemic occurred in Turkey as four waves. Von Bertalanffy’s growth model appears to be an explanatory model for all pandemic waves according to the performance measures.
Conclusion: It was observed that the COVID-19 pandemic spread in Turkey in four waves. Investigating COVID-19 and similar pandemics using versatile and multidisciplinary methods is of great importance in understanding and predicting the behavior of pandemics.

2022: Are We Back to the pre-COVID-19 Pandemic Period in the Management of out-of-Hospital Cardiac Arrest?

2023-09-25T14:47:53+00:00

Introduction: The COVID-19 pandemic caused a significant strain on the Emergency system, particularly for time-dependent diseases like Out of Hospital Cardiac Arrest (OHCA). Studies have shown an increase in the incidence of OHCA during different waves of the pandemic, but there is limited evidence on how survival rates and rescue efforts have been affected in the post-pandemic period.
Methods: We performed a retrospective observational cohort study of all OHCA rescues by AREU (Agenzia Regionale Emergenza Urgenza), in the Lombardy region in March in three different years (2019, 2021 and 2022). We used rescue mission data collected in AREU’s database, where logistic information of patient rescue missions managed by the Lombardy Region’s 112 system is recorded.
Results: This study was an epidemiology analysis of OHCA after the pandemic. The results showed no significant changes in the probability of receiving bystander cardiopulmonary resuscitation (22.5% vs 24.0%; p=0.41) and public access defibrillation (3.6 vs 3.2; p=0.50) compared to pre-pandemic period. However, there was a decrease in the probability of ROSC (11.5% vs 6.2%; p<0.01).
Conclusion: According to our analysis, there appears to be a return to the pre-pandemic phase with regard to the OHCA network. However, it remains to be pointed out that a careful study of disease networks is essential to understand the resilience of our health system and to understand whether we have returned to a system similar to the pre-pandemic phase after the COVID-19 pandemic.

Study Design and Research Protocol for diagnostic or prognostic studies in the Age of Artificial Intelligence: A Biostatistician’s Perspective

2024-01-19T14:14:31+00:00

Introduction: As the integration of Artificial Intelligence (AI) in healthcare continues to advance, the need for rigorous study design and research protocols tailored to diagnostic and prognostic studies becomes paramount.
Aim: The primary objective of this work is to highlight the biostatistician’s point of view about the key points of the research protocol involving AI.
Methods: Assessing the current state-of-the-art guidelines, we outline the methodological challenges faced by biostatisticians when collaborating on research protocols in the era of AI-driven medical research.
Results: The proposed overview on research protocol involving AI elucidates key considerations in study design, encompassing evaluations of data quality, analysis of biases, methodological approaches, determination of sample size, and validation strategies tailored specifically to AI applications. This position paper underscores the pivotal role of strong statistical frameworks in ensuring the reliability, validity, and applicability of findings derived from AI-based diagnostic and prognostic models. Moreover, the paper seeks to highlight the critical importance of incorporating transparent reporting standards to enhance the reproducibility and clarity of AI-driven studies.
Conclusions: By offering a comprehensive biostatistician’s viewpoint, this paper strives to significantly contribute to the methodological progression of diagnostic and prognostic studies in the era of Artificial Intelligence.

Analysis of High Levels of Urine Proteins as a Sign of Impaired Kidney Function in Communities Around a Nickel Mining Industry in Morosi District, Southeast Sulawesi

2024-03-08T15:05:59+00:00

The study is proposed to analyse the influence of individual and environmental factors on protein urine levels as a parameter for impaired kidney function in the community around Nickel mining industry in Morosi Sub-District, Southeast Sulawesi Indonesia. This quantitative study used a cross sectional study design. The population was people in 3 villages around the nickel mining industry aged 20-59 years. The sample size was 61 people using simple random sampling technique. Independent variable was age as representation of individual factor and environmental factors including house condition, water quality, waste management and household liquid waste. Protein urine as a parameter for impaired kidney function as the dependent variable. Data was collected by questionnaire and taking urine samples. Data were analysed using the multinomial logistic regression test with a significance level of 95%. The results show that the determinant of proteinuria levels in communities around the Morosi nickel mining industry was age (p=0.038<0.05), the water source quality (p=0.002<0.05). Solid waste management (p=0.05) and waste water management (p-value=0.045< 0.05). Meanwhile, the house condition had no effect on high proteinuria levels (p value> 0.05). In conclusion, impaired kidney function in communities around the Morosi nickel mining industry due to age, poor quality of water sources and poor management of solid waste and household liquid waste.

Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA)

2024-02-14T09:44:29+00:00

This paper delves into the realm of advanced data analysis, focusing on two powerful dimensionality reduction methods: Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA). Methodological marvels in their own right, these approaches are scrutinized for their unique properties and applications across diverse domains. We navigate through the intricacies of their algorithms and explore how they unveil patterns within complex datasets. The comparative analysis highlights the strengths and weaknesses of DPCA and DMCA, shedding light on their distinct contributions to the analytical landscape. This paper serves as a comprehensive guide for researchers and analysts seeking deeper insights into these cutting-edge techniques for dimensional reduction.

Social and Environmental Factors Influencing COVID-19 Transmission and Mortalities in Developing and Developed Nations

2024-01-08T09:10:07+00:00

Background: The study sought to establish environmental and social factors that influenced the transmission and mortalities of COVID-19 in developing and developed nations. The factors that were assessed included temperature, average age of the population, urbanization, population density, and percentage of old-aged people in the population. The dependent variables were COVID-19 transmission and COVID-19-related deaths.
Methods: The study employed a pragmatism research philosophy. It also relied on a deductive research approach and a descriptive research design. It adopted a mixed-method approach as it used both qualitative and quantitative data. It was a cross-sectional study, given its data measurement at a particular point in time. Data was analyzed and presented using descriptive techniques.
Results: Statistical analyses were conducted to quantify the relationships between various factors and COVID-19 outcomes. A Kendall’s Tau test revealed a robust negative correlation between COVID-19 cases and temperature (Tb = -0.560, p<0.005). This result was further confirmed by Spearman’s rank correlation, showing a strong negative correlation with r(13) = -0.684, p<0.007. Similarly, a strong negative correlation was observed between COVID-19 deaths and average annual temperature using both Kendall’s Tau (Tb = -0.495, p<0.014) and Spearman’s rank correlation (r(13) = -0.648, p<0.012). Age emerged as a significant factor, with a strong positive correlation found between age and both COVID-19 infections (Tb = 0.516, p<0.010; r(13) = 0.670, p<0.009) and COVID-19-related mortalities (r(13) = 0.516, p<0.029). Urbanization was also positively correlated with COVID-19 infections (Tb = 0.530, p<0.008; r(13) = 0.640, p<0.014) and COVID-19 deaths (Tb = 0.398, p<0.048; r(13) = 0.561, p<0.037). Interestingly, no significant correlation was found between population density and COVID-19 infections or deaths in both developed and developing countries, as evidenced by Kendall’s Tau (TB = 0.331, p<0.1; Tb = 0.133, p<0.511) and Spearman’s rank correlation (r(13) = 0.425, p<0.130; r(13) = 0.161, p<0.583), respectively. Moreover, the percentage of elderly individuals in a country exhibited a strong positive correlation with both COVID-19 infections (Tb = 0.464, p<0.021; r(13) = 0.642, p<0.013) and COVID-19-related deaths (r(13) = 0.541, p<0.046).
Conclusion: The study focused on social, demographic, and environmental factors influencing COVID-19 incidence and mortality in developing and developed nations. The study highlights significant COVID-19 transmission and mortality disparities between developed and developing countries. Developed countries exhibited higher infection and mortality rates, coupled with elevated death rates per million and infection rates per million, as compared to their developing counterparts. The research identified a correlation between lower average annual temperatures and increased mortality in developed countries. Contrary to this, high average annual temperatures were associated with a decline in COVID-19 infections.
Moreover, developed countries, characterized by higher urbanization levels, population densities, and percentages of aged individuals, experienced elevated COVID-19 infection rates. The study also unveiled a positive correlation between age and COVID-19 infections, with developed countries hosting significantly older populations than their developing counterparts. However, population density did not clearly correlate with COVID-19 infections or deaths.

Pregnancy-associated Cancers: A Narrative Review

2024-01-15T09:56:02+00:00

Pregnancy-associated cancers are malignancies diagnosed during pregnancy or within one year of delivery or abortion. These cancers present unique challenges because of the delicate balance required for maternal-fetal health. Diagnosis can be made complex by physiological changes associated with pregnancy, and treatment decisions must take into account potential harm to the foetus. Multidisciplinary collaboration between oncologists and obstetricians is essential. Despite the complexities, early detection and tailored management can optimise outcomes for both mother and child. A systematic approach is currently lacking; further research into prenatal exposure to maternal cancer is recommended to formulate evidence-based guidelines for the management of cancer in pregnancy.

Healthy and Unhealthy Plant-Based Diets and Body Weight in Breast Cancer Survivors: A Narrative Review

2024-01-11T10:41:19+00:00

Obesity in breast cancer (BC) survivors increase the risk of BC recurrence, second primary BC, BC-specific mortality, and overall mortality. Guidelines for BC survivors encourage healthy lifestyles by promoting healthy diets, engage in physical activity and avoid weight gain to achieve longer survival and improved quality of life. In recent years, there has been a growing interest in the possible beneficial role of plant-based diets in body weight control and in BC risk and prognosis. Plant-based diets can be evaluated using dietary indices which provide a quantitative measure of how closely an individual’s diet aligns with a plant-based dietary pattern. However, there is a need to distinguish plant-based diets in healthy and unhealthy. This approach would address a research gap that often overlooks the quality and specific types of plant foods consumed. The aim of this narrative review is to analyze how a plant-based diet may impact on body weight in BC survivors, synthesizing existing evidence and discussing the potential mechanisms and implications. The findings suggest the importance of considering the quality of plant-based diets, as some may include vegetarian foods with a low nutritional profile which may negatively impact on body weight. This aspect could be crucial in preventing weight gain in women with BC, as body weight is considered a risk factor for poor BC prognosis and reduced survival.