Health Decisions Research Articles

Predicting particulate matter ( ) levels in Morocco: a 5-day forecast using the analog ensemble method.

The accurate prediction of particulate matter ( ) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of over Morocco for 5days. The analog ensemble (AnEn) and the bias correction (AnEnBc) techniques were employed to post-process forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the root mean squared error (RMSE) decreased from 63.83 in the original forecasts to 44.73 with AnEn and AnEnBc, while the mean absolute error (MAE) reduced from 36.70 to 24.30 . Additionally, the coefficient of determination ( ) increased more than twofold from 29.11 to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. The integrating reanalysis data and the utilization of the AnEn substantially improved the accuracy of 5-day forecasting in Morocco. This is the first application of this approach in Morocco and the Middle East and North Africa (MENA) and has the potential for translation into early and more accurate warnings of pollution events. The application of such approaches in environmental policies and public health decision-making can minimize the health impacts of air pollution.

Epidemiological landscape in Iran: A qualitative analysis of strengths, weaknesses, and growth potential

ObjectivesEpidemiology deals with all aspects of wellness and diseases in communities. Due to the crucial contribution of epidemiology in basic and applied research, operationalization of social and health constellations, and advancing multidisciplinary science, this study aims to Epidemiological Landscape in Iran. Study designQualitative study. MethodsThis qualitative study employed semi-structured interviews with a purposive sampling of graduate students, faculty members, and professors with at least five years of experience in epidemiology from various medical universities. The research team, comprising three epidemiologists and one graduate student, ensured the validity of the interview guide. Data saturation was achieved after 17 interviews, focusing on challenges and opportunities in epidemiology, influenced by factors like health ministry policies and societal attitudes. Participants were asked about the ideal state of epidemiology, existing gaps, and their main concerns. The principal investigator confirmed understanding of the concepts presented, and data credibility was assessed through various criteria. The analysis involved coding participants' responses and categorizing them to streamline the findings into main and subcategories. All the qualitative analyses were conducted using MAXQDA (version 2020). ResultsThe finding highlight epidemiology's critical role in addressing health problems, assessing population health, and diagnosing main issues. The COVID-19 pandemic provided unique opportunities to utilize epidemiological knowledge, which the IEA should amplify by employing epidemiologists in decision-making. However, challenges include limited job/training opportunities. Key limitations are restricting epidemiology to research, isolating it from health decision-making, lack of funding and resources at some universities, and ineffective public communication during pandemic. Strengths are experienced epidemiologists, the IEA, enthusiastic students, and active participation in managing the pandemic. Opportunities include a growing need for epidemiology among policymakers, positive feedback on its role in infectious disease control, and research opportunities for students. ConclusionsThe study identified significant challenges but also strengths and opportunities for epidemiology in Iran. Key recommendations include strengthening collaboration with policymakers, improving education, supporting young epidemiologists, and reinforcing the IEA.

Get BusActive!: Protocol of a single-blinded randomised controlled trial incentivising public transport use for physical activity gain among young people and adults.

Population level physical activity generally does not meet recommended targets. Compared with private motor vehicle users, public transport users tend to be more physically active and financial incentives may encourage more public transport use, but these relationships are under-investigated. This paper describes the protocol of a randomised controlled trial that aimed to determine the effect of financially incentivising public transport use on physical activity in a regional Australian setting. Get BusActive! is a 9.5-month single-blinded randomised controlled trial. A convenience sample of Tasmanians aged ≥15 years will be randomised to a 14-week incentive-based intervention (bus trip target attainment rewarded by bus trip credits and weekly supportive text messages) or an active control following baseline measures and will be followed up ∼24 weeks later (maintenance phase). Both groups will receive written physical activity guidelines. The primary outcome is change in accelerometer-measured steps/day from baseline to immediately post intervention phase and maintenance phase. Secondary outcomes are change in: smartcard-measured bus trips/week; measured and self-reported minutes/week of physical activity and sitting; transport-related behaviour (using one-week travel diary), perspectives (e.g. enablers/barriers) and costs; health. Linear mixed model regression will determine group differences. Participant-level process evaluation will be conducted and intervention cost to the public transport provider determined. Get BusActive! will fill an important knowledge gap about the causal relationship between financially incentivised public transport use and physical activity-the findings will benefit health and transport-related decision makers. ACTRN12623000613606. U1111-1292-3414.

Leveraging digital marketing for health behavior change: A model for engaging patients through pharmacies

Leveraging digital marketing for health behavior change can significantly enhance patient engagement, especially when deployed through pharmacies. Pharmacies play a pivotal role in public health by serving as accessible points of care where patients can receive critical health information and support for managing various conditions. This paper examines the intersection of behavioral theories, such as the Health Belief Model and Social Cognitive Theory, with digital marketing strategies tailored for health engagement. It discusses how pharmacies can effectively use targeted content, social media outreach, and personalized messaging to influence health behaviors. Through a pharmacy-centered approach, digital initiatives like mobile applications, loyalty programs, and telehealth promotions offer new avenues for reaching patients and encouraging proactive health decisions. However, challenges such as data privacy concerns, the risk of misinformation, and regulatory compliance pose barriers to successful implementation. To address these issues, this paper presents recommendations that include secure data practices, evidence-based content, and inclusive digital solutions. Ultimately, by combining theoretical insights with practical digital marketing strategies, pharmacies can foster healthier behaviors, improve patient outcomes, and support broader public health goals.

Smart Machine Learning Analysis and Blockchain Healthcare Platform

The Smart Machine Learning Medical Analysis and Blockchain Healthcare Platform (SMLMAP) represents a transformative approach to medical diagnostics and data management by integrating advanced machine learning algorithms with blockchain technology. This platform is designed to accurately diagnose seven critical diseases, achieving impressive diagnostic outcomes across various conditions, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia. SMLMAP employs Random Forest algorithms for text data analysis and Convolutional Neural Networks (CNNs) for image data, ensuring rapid and precise diagnostic results. In addition to enhancing diagnostic capabilities, SMLMAP addresses significant privacy and security concerns inherent in healthcare data management. The integration of blockchain technology facilitates secure, transparent, and tamper-proof storage of patient records, empowering individuals with greater control over their medical information. This dual approach not only improves the accuracy of diagnoses but also fosters trust between patients and healthcare providers by safeguarding sensitive data against unauthorized access. Key functionalities of SMLMAP include the ability to schedule doctor appointments conveniently through the platform, ensuring that patients can receive timely medical attention. Additionally, the application provides users with a weekly healthcare magazine, delivering valuable information on health tips, disease management, and wellness advice to promote informed health decisions. By leveraging the strengths of machine learning and blockchain, SMLMAP paves the way for a more efficient, accurate, and secure healthcare ecosystem. This innovative platform not only enhances the quality of medical diagnostics but also represents a significant step forward in addressing the critical challenges of data privacy and security in the healthcare sector. The findings underscore the potential of SMLMAP to revolutionize patient care and data management in the evolving landscape of healthcare technology.

PERS: Personalized environment recommendation system based on vital signs

The integration of the Internet of Things (IoT) in healthcare has facilitated real-time monitoring of vital signs and environmental conditions. However, existing systems often lack personalized recommendations that consider the interplay between these factors. This work introduces the Personalized Environment Recommendation System (PERS), which leverages a portable device to continuously collect data on key health metrics, including pulse rate and body temperature, alongside environmental parameters. Utilizing Artificial Neural Networks, PERS analyzes the data to generate tailored health recommendations for users. Experimental results demonstrate an accuracy of 98.7%, highlighting the system’s effectiveness in enhancing patient care and supporting informed health decisions. The findings suggest that PERS can significantly improve health monitoring by providing actionable insights based on individual health profiles and environmental contexts.

Social Location and Decision-Making Among Women Living with HIV in the Southern United States: An Intersectional Approach

HIV care engagement and antiretroviral therapy (ART) adherence interventions aimed at decreasing viral suppression disparities for women living with HIV (WLWH) in the Southern United States (i.e., the South) are few and seldom consider diverse social locations. These refer broadly and dynamically to contextual factors and the position people occupy in a social hierarchy based on intersecting systems of oppression and social determinants of health like gender, race/ethnic, and class inequities; geographic location; and HIV-related stigma. Using an intersectional approach, we conducted in-depth interviews and used a phased approach to directed content analysis to describe women’s perceptions of their social location and how it impacts their decision-making about HIV care engagement and ART adherence. Participants were recruited to participate from a broad geographic area and represented the diverse social locations occupied by WLWH in the South. Findings from 40 in-depth interviews include descriptions of how geographic context impacts HIV care engagement and medication adherence through access to care, quality of care, and the place-based context of day-to-day experiences of living with HIV. Participants spoke of multilevel power relationships based on their gender and racial identities, and how social determinants and intersecting identities occur simultaneously and vary as a function of one another to impact health and HIV care decision-making. The findings offer a nuanced understanding of how WLWH perceive their contextually specific social locations and make critical decisions about their HIV care engagement and medication adherence.

Correction: Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Correction: Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Parent-Child Privilege as Resistance

In the wake of Dobbs v. Jackson Women’s Health Organization, minors face increasing restrictions in accessing reproductive care. The majority of pregnant minors consult their parents before obtaining abortions. Although the parent-child relationship is the first to exist and is often where children turn for guidance, there is no recognized parent-child privilege in the majority of states. These healthcare conversations between parent and child now may hold criminal implications. This Article provides a novel contribution in exploring parent-child communications within the health and reproductive decision-making contexts, arguing privilege is essential to protect minors in accessing care post-Dobbs. In the process, this Article provides a broader theoretical contribution—introducing a new conceptual framework of “privilege as resistance” that envisions evidentiary parent-child privilege as a tool to oppose unjust systems and laws. Privileges are generally disfavored in evidentiary law as impeding the truth; however, this Article critiques the prioritization of the carceral system’s search for truth over the relationship between the parent and child. Parent-child privilege as resistance is informed by a history filled with the separation of families and denial of intimate spaces for marginalized individuals, in particular enslaved individuals and Indigenous people. Building on this context, the Article argues that despite using parents to legitimize judicial processes, the justice system fails to recognize them and shield their communications. Privilege as resistance can enable parents to be an integral part of anti-carceral—and far more successful—approaches to juvenile justice, such as restorative justice models. With this critical lens, the Article imagines a contemporary parent-child privilege that protects a child’s privacy, bodily autonomy, and direction over their lives.

Comprehensive assessment of COVID-19 case fatality rate and influential factors in Khuzestan Province, Iran: a two-year study

BackgroundThe emergence of a new pandemic SARS-CoV-2 (COVID-19) resulted in a high mortality rate across the world. This study sought to comprehensively analyze the Case Fatality Rate (CFR) associated with COVID-19 in the Khuzestan province of Iran”. The primary objective was to discern patterns and determinants influencing CFR, shedding light on the evolving impact of the pandemic on morbidity and mortality.MethodsA retrospective examination was performed on a dataset encompassing confirmed COVID-19 cases and related fatalities in Khuzestan. Data, spanning from December 2020 to April 2022, underwent rigorous statistical analysis. Demographic variables, comorbidities, and temporal trends were scrutinized to identify key factors influencing CFR.ResultsPreliminary findings revealed dynamic shifts in CFR, capturing the nuanced nature of the pandemic over time. Demographic nuances, particularly age and gender, emerged as significant determinants impacting CFR, the reported CFR of COVID-19 in Khuzestan province was 1.79%.ConclusionThis study contributes critical insights into the CFR landscape of COVID-19 in Khuzestan, providing a foundation for evidence-based decision-making in public health. The identified factors influencing mortality rates offer valuable information for tailoring interventions and optimizing resource allocation. Continuous monitoring and further investigations are recommended to adapt strategies to the evolving nature of the pandemic.

Impact of COVID-19 vaccination on preventive behavior: The importance of confounder adjustment in observational studies.

The COVID-19 pandemic has underscored the critical role of observational studies in evaluating the effectiveness of public health strategies. However, although many studies have explored the true impact of vaccination on preventive behavior, their results may be skewed by potential biases and confounding variables. This study examines the application of covariate adjustment and propensity score (PS) estimation, particularly through inverse probability treatment weighting (IPTW), to assess their performance in reducing bias in a framework featuring ordinal outcomes and cumulative logistic regression models, as commonly used in observational studies related to social sciences and psychology. Before applying these methods to the case study, we conducted a simulation study that accounted for the presence or absence of model misspecification in an observational scenario with ordinal outcomes, binary treatment, and a continuous confounder. Our findings demonstrate the effectiveness of combining covariate adjustment with PS methods in reducing bias and improving causal inference. These methods were subsequently applied to an Italian observational study on COVID-19 vaccine hesitancy conducted during the initial phase of the vaccination campaign (April-May 2021). Our analysis revealed that vaccination status had a limited short-term impact on the adoption of preventive measures. This study highlights the importance of employing appropriate adjustment techniques in observational research, particularly when evaluating complex behavioral outcomes. The results support the combined use of covariate adjustment and PS methods to enhance the reliability of findings, ultimately contributing to more informed public health decision-making.

Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.

The value of environmental surveillance for pandemic response

Environmental sampling surveillance (ESS) technologies, such as wastewater genomic surveillance and air sensors, have been increasingly adopted during the COVID-19 pandemic to provide valuable information for public health response. However, ESS coverage is not universal, and public health decision-makers need support to choose whether and how to expand and sustain ESS efforts. This paper introduces a model and approach to quantify the value of ESS systems that provide leading epidemiological indicators for pandemic response. Using the COVID-19 pandemic as a base-case scenario, we quantify the value of ESS systems in the first year of a new pandemic and demonstrate how the value of ESS systems depends on biological and societal parameters. Under baseline assumptions, an ESS system that provides a 5-day early warning relative to syndromic surveillance could reduce deaths from 149 (95% prediction interval: 136–169) to 134 (124–144) per 100,000 population during the first year of a new COVID-19-like pandemic, resulting in a net monetary benefit of $1,450 ($609-$2,740) per person. The system’s value is higher for more transmissible and deadly pathogens but hinges on the effectiveness of public health interventions. Our findings also suggest that ESS systems would provide net-positive benefits even if they were permanently maintained and pathogens like SARS-Cov-2 emerged once every century or less frequently. Our results can be used to prioritize pathogens for ESS, decide whether and how to expand systems to currently uncovered populations, and determine how to scale surveillance systems’ coverage over time.

Detection of Real-Time Changes in Direction of COVID-19 Transmission Using National- and State-Level Epidemic Trends Based on Rt Estimates - United States Overall and New Mexico, April-October 2024.

Public health practitioners rely on timely surveillance data for planning and decision-making; however, surveillance data are often subject to delays. Epidemic trend categories, based on time-varying effective reproductive number (Rt) estimates that use nowcasting methods, can mitigate reporting lags in surveillance data and detect changes in community transmission before reporting is completed. CDC analyzed the performance of epidemic trend categories for COVID-19 during summer 2024 in the United States and at the state level in New Mexico. COVID-19 epidemic trend categories were estimated and released in real time based on preliminary data, then retrospectively compared with final emergency department (ED) visit data to determine their ability to detect or confirm real-time changes in subsequent ED visits. Across the United States and in New Mexico, epidemic trend categories were an early indicator of increases in COVID-19 community transmission, signifying increases in COVID-19 community transmission in May, and a confirmatory indicator that decreasing COVID-19 ED visits reflected actual decreases in COVID-19 community transmission in September, rather than incomplete reporting. Public health decision-makers can use epidemic trend categories, in combination with other surveillance indicators, to understand whether COVID-19 community transmission and subsequent ED visits are increasing, decreasing, or not changing; this information can guide communications decisions.

Rejection of Federal Guidance by Southern US States and Projected Consequences for Ending the HIV Epidemic.

Evidence-based HIV programming focused on populations at risk of HIV is critical for sustainable disease prevention. In response to the Tennessee Department of Health decision to reject federal HIV guidance, the present study examines the potential impact of Southern US states adopting policies that direct HIV prevention and testing efforts away from populations "disproportionately affected" (DA) by HIV toward populations "non-disproportionately affected" (NDA). Descriptive and projection analyses with publicly available data explored the influence of policies emphasizing NDA populations on HIV cases. Across the Southern US, DA populations (cisgender women, men who have sex with men, transgender persons, and people who inject drugs) represent the absolute majority (90%) of diagnosed HIV cases, whereas NDA populations (pregnant women, infants, first responders, and human trafficking victims) represent only a small proportion (2%) of diagnosed HIV cases. Estimated projections show avoidable HIV cases among DA populations in the Southern US alone could aggregate to over 32,000 by 2030 if prevention efforts exclusively concentrate on NDA populations, which approximates the current national annual incidence of 36,000 HIV cases. Prevention efforts aimed at DA populations compared with NDA populations could reduce new infections across the Southern US by 47% versus 1%, respectively, by 2030. Policies disregarding epidemiological data may hinder efforts to end the HIV epidemic, both regionally and nationally.

Unlocking data: Decision-maker perspectives on cross-sectoral data sharing and linkage as part of a whole-systems approach to public health policy and practice.

Secondary data from different policy sectors can provide unique insights into the social, environmental, economic and political determinants of health. This is especially pertinent in the context of whole-systems approaches to healthy public policy, which typically combine cross-sectoral collaboration with the application of theoretical insights from systems science. However, the sharing and linkage of data between different sectors are still relatively rare. Previous research has documented the perspectives of researchers and members of the public on data sharing, especially healthcare data, but has not engaged with relevant policy and practice decision-makers. We sought to work collaboratively with decision-makers relevant to healthy public policy and practice in Scotland to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities. We facilitated three sequential stakeholder workshops with 20 participants from local and central government, public health teams, Health and Social Care Partnerships, the third sector, organisations which support data-intensive research and public representatives from across Scotland. Workshops were informed by two scoping reviews (carried out in June 2021) and three case studies of existing cross-sectoral linkage projects. Workshop activities included brainstorming of factors that would help participants make better decisions in their current role; reflective questions on lessons learnt from the case studies; and identifying and prioritising recommendations for change. Findings were synthesised using thematic analysis. Scotland; public and third sector data. Based on the workshops, and supported by the reviews and case studies, we created a visual representation of the use of evidence, and secondary data in particular, in decision-making for healthy public policy and practice. This covered three key overarching themes: differing understandings of evidence; diverse functions of evidence; and factors affecting use (such as technical, political and institutional, workforce and governance). Building on this, workshop participants identified six guiding principles for cross-sectoral data sharing and linkage: it should be pragmatic; participatory; ambitious; fair; iterative; with holistic and proportionate governance. Participants proposed 21 practical actions to this end, including: a strategic approach to identifying and sharing key data sets; streamlining governance processes (e.g. through standardised data sharing agreements; central data repositories; and a focus on reusable data resources) and building workforce capacity. To make these possible, participants identified a need for strong political and organisational leadership as well as a transparent and inclusive public conversation. Participation from some stakeholders was limited by workload pressures associated with the COVID-19 pandemic. No consensus was reached on the impact, effort, and/or timing of some recommendations. Findings were closely informed by the Scottish context but are nonetheless likely to be relevant to other jurisdictions. There is broad consensus among key stakeholders that linked cross-sectoral data can be used far more extensively for public health decision-making than it is at present. No single change will lead to improved use of such data: a range of technical, organisational and political constraints must be addressed. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme as award number NIHR133585.

Enhancing geographical access to cardiovascular disease healthcare services in Lagos State, Nigeria

Abstract Cardiovascular diseases (CVDs) are the leading cause of global mortality, and three-quarters of the world’s deaths from CVDs occur in low- and middle-income countries. Improving spatial access to care facilities is a way of reducing the disease burden through early detection and treatment. Determining the number of facilities to provide and the locations in order to improve accessibility is central to healthcare facility planning. The maximal covering location problem (MCLP) was used in the identification and determination of where to place additional CVD treatment facilities in Lagos State for better accessibility. The primary objective of this study was to optimize the placement of CVD facilities to maximize coverage for both patients and non-patients alike. The optimization model utilized data on the population of Lagos State, the locations of CVD patients, the location of existing CVD care facilities, and the locations of general hospitals (GHs) within the state. The MCLP implementation was carried out using the maxcovr. The results of the models indicated that the addition of two facilities at the Isolo and Ile-Epo GHs would enhance CVD patient coverage from 70 to 87.4%, reducing the average travel distance from 9,038 to 6,466 m. Similarly, the introduction of two facilities at the Ikorodu and Ajegunle GHs improved population coverage from 40.3 to 70.8%, reducing the average travel distance of 18,788 m by over 5,000 m. These findings are expected to offer valuable insights for public health decision-makers who face the challenge of prioritizing limited budgets.

Comparative risk assessment modeling of cardiovascular and all-cause burden attributable to sitting time and physical inactivity: evidence from Argentina.

Although there is evidence that sitting time (ST) and insufficient physical activity (PA) are associated with premature mortality, the burden of cardiovascular disease (CVD) and all-cause mortality (ACM) attributable to the combined effects of ST and PA in counterfactual scenarios is limited. Potential impact fractions (PIFs) were used to calculate premature deaths (PDs) and disability-adjusted life years (DALYs). Cause-eliminated life tables were utilized to estimate health-adjusted life expectancy (HALE) gains. Monte Carlo simulations were performed for uncertainty analysis. The theoretical minimum risk exposure level (ST < 4 $\mathrm{h} \cdot{\mathrm{d}}^{-1}$, PA > 65 $\mathrm{min} \cdot{\mathrm{d}}^{-1}$) could prevent 16.7% of CVD deaths and 12.3% of all-cause deaths annually. This would save 669 to 2,630 DALYs per 100,000 and increase healthy life years by 0.57 to 2.94. Increasing PA to > 65 $\mathrm{min} \cdot{\mathrm{d}}^{-1}$ while maintaining ST could yield gains in HALE from 0.49 (CVD) to 2.60 (ACM) years. Reducing ST to < 4 $\mathrm{h}\cdot{\mathrm{d}}^{-1}$ while keeping PA constant could lead to gains in HALE from 0.07 (CVD) to 0.34 (ACM) years. A 50% reduction in suboptimal ST (≥ 4 $\mathrm{h}\cdot{\mathrm{d}}^{-1}$) doubled HALE gains, ranging from 0.11 to 0.63years. Public health decision-makers should prioritize vulnerable populations, including older adults and individuals with inadequate PA levels.

Theoretical perspectives on biostatistics and its multifaceted applications in global health studies

Biostatistics plays a crucial role in global health studies by providing the tools necessary for analyzing complex health data and informing evidence-based public health decisions. This theoretical review explores the foundations of biostatistics and its diverse applications in the context of global health. At its core, biostatistics utilizes mathematical and statistical principles to analyze health data, enabling researchers to identify patterns, determine risk factors, assess interventions, and predict health outcomes across populations. Key theoretical concepts, including probability theory, statistical inference, and regression models, form the backbone of biostatistical analysis and are integral to understanding the dynamic relationships between health determinants and outcomes. The review highlights the various applications of biostatistics in global health, focusing on areas such as disease surveillance, epidemiology, and public health intervention evaluation. Biostatistical techniques are essential for analyzing epidemiological data, monitoring disease trends, and evaluating the effectiveness of health policies and interventions on a global scale. Furthermore, advanced statistical methods, including machine learning and Bayesian approaches, are becoming increasingly important in global health research. These methods allow for the analysis of large-scale data, predictive modeling, and more precise risk assessments, particularly in the context of emerging diseases, genetic research, and environmental health studies. Despite its strengths, biostatistics faces challenges in global health, including issues related to data quality, ethical considerations, and cross-cultural variations in health outcomes. Addressing these challenges is critical for ensuring accurate and equitable health assessments. Looking forward, biostatistics continues to evolve with innovations in data science, big data analytics, and real-time health data collection, making it an indispensable tool for improving global health outcomes and supporting health policies. This review emphasizes the theoretical underpinnings of biostatistics while highlighting its transformative potential in global health research and practice. Keywords: Theoretical Perspectives, Biostatistics, Multifaceted Applications, Global Health Studies.

Beyond health: exploring women’s socio-cultural motivations for HPV vaccination

ABSTRACT Purpose Autonomous motivation is critical to motivating individuals to adopt healthy behaviours. While many studies have explored the medical factors that influence women’s autonomous motivation to receive HPV vaccination, sociocultural factors have received less attention. Therefore, this study analyses women’s autonomous motivation for HPV vaccination from a sociocultural perspective to gain insight into the generation of women’s health decisions. Methods This study conducted in-depth interviews with 19 women at their peak age of infection who had been vaccinated against HPV to examine the socio-cultural factors that drove them to receive HPV vaccination. It used NVivo12 to analyse qualitative data obtained from interviews. Results The results showed that (1) Demonstrating Personal Independence, (2) Asserting Reproductive Autonomy, (3) Acknowledging Sexual Needs, and (4) Showcasing Health Literacy, Economic Power, and Social Connections were autonomous motivations for women to receive HPV vaccination. Conclusions Based on these motivations, future vaccine promotion programmes should demonstrate the role of the HPV vaccine for women to become autonomous individuals, as well as clear up misconceptions between the HPV vaccine and sexual shame and reduce the monetary burden of the vaccine to build a friendly and egalitarian vaccination environment.