Human behavior in the environment: a justice for vulnerable populations approach

Re-emergence of infectious diseases associated with the past

Pandemics are not merely medical crises; they are deeply embedded in the fabric of social life that reshape societies, institutions, and human behaviour. This paper explores the social dimensions of pandemics across history, from the Black Death of the 14th century to the ongoing COVID-19 crisis. Using a comparative historical approach, it examines how different societies have responded to health emergencies, the stigmas attached to disease, and the role of state, class, gender, and religion in shaping public reactions and containment strategies. The study highlights recurring patterns, such as scapegoating of marginalized groups, breakdown of social trust, and the reinforcement or transformation of political authority. It also reveals how pandemics have often accelerated social change, including shifts in labour relations, urban planning, and health governance. Through sociological theories like structural functionalism, symbolic interactionism, and conflict theory, the paper analyses how fear, solidarity, and inequality intersect during times of mass illness. Focusing on COVID-19, this paper argues for a more integrated, justice-oriented approach to pandemic response, one that incorporates historical lessons and centres on social resilience. The paper also investigates the impact of lockdowns, digital surveillance, social distancing, and the infodemic, particularly on vulnerable populations. It concludes that understanding pandemics through a social lens is essential not only for crafting inclusive health policies but also for building more resilient and equitable societies. The paper advocates for a multidisciplinary approach in future pandemic preparedness that places social justice, community trust, and ethical governance at its core.

BackgroundSpatial and temporal identification of malaria-endemic areas is a key component of vector-borne disease control. Strategies to target the most vulnerable populations, the periods of high transmission and the most affected geographical areas, should make vector-borne disease control and prevention programmes more cost-effective. The present study focuses on the spatial and temporal dynamics of malaria cases and the exogenous factors influencing the transmission in an area with pyrethroid-resistant mosquito vector populations.MethodsA prospective cohort study of 1806 children under 10 years of age was conducted over 20 months to assess the risk of malaria incidence in the Cove-Zagnanado-Ouinhi (CoZO) health zone located in southern Benin. Childhood malaria data were used to identify malaria hotspots according to months of follow-up using spatial scanning methods based on the Kulldoff algorithm. Stability scores were calculated by season to assess incidence heterogeneity. Incidence values by month were aggregated with meteorological data; and demographic data were merged to detect cross-correlation between incidence and meteorological variables. Generalized equation estimators were chosen for their ability to handle intra-group correlation, ensuring robust and interpretable results despite the complexity of the data to identify factors explaining the spatio-temporal heterogeneity of malaria incidence in the CoZO health zone.ResultsMalaria incidence ranged from 1.41 (95% IC 0.96–2.08) to 13.91 (95% IC 12.22–15.84) cases per 100 child-months. Spatial heterogeneity in malaria transmission hotspots was observed over the study period, with relative risks ranging from 1.59 (p-value = 0.032) to 16.24 (p-value = 0.002). There was a significant negative association (correlation coefficient = − 0.56) between malaria incidence and temperature; and a slightly positive association (correlation coefficient = 0.58) between malaria incidence and rainfall. A significant association between malaria incidence with average house altitude (adjusted incidence rate ratio [aIRR] 1 (95% IC 0.99–1) P < 0.001), soil type aIRR 0.54 (0.39–0.75) p < 0.001 and temperature (incidence rate ratio [IRR] 0.69 (0.66–0.73) p < 0.001).ConclusionThis study uses innovative technologies such as remote sensing and geographic information systems (GIS) to analyse the environmental, meteorological and geographical factors influencing malaria transmission, thereby identifying high-risk areas and associated factors. It demonstrates that these tools improve the accuracy of control strategies, while highlighting the crucial role of the environment and human behaviour, paving the way for more targeted interventions against malaria and other vector-borne diseases.

Extreme precipitation events (EPE) change the natural and built environments and alter human behavior in ways that facilitate infectious disease transmission. EPEs are expected with high confidence to increase in frequency and are thus of great public health importance. This scoping review seeks to summarize the mechanisms and severity of impacts of EPEs on infectious diseases, to provide a conceptual framework for the influence of EPEs on infectious respiratory diseases, and to define areas of future study currently lacking in this field. The effects of EPEs are well-studied with respect to enteric, vector-borne, and allergic illness where they are shown to moderately increase risk of illness, but not well-understood in relation to infectious respiratory illness. We propose a framework for a similar influence of EPEs on infectious respiratory viruses through several plausible pathways: decreased UV radiation, increased ambient relative humidity, and changes to human behavior (increased time indoors and use of heating and cooling systems). However, limited work has evaluated meteorologic risk factors for infectious respiratory diseases. Future research is needed to evaluate the effects of EPEs on infectious respiratory diseases using individual-level case surveillance, fine spatial scales, and lag periods suited to the incubation periods of the disease under study, as well as a full characterization of susceptible, vulnerable, and sensitive population characteristics.

In 1976, two years before the Alma Ata Declaration, the then Director-General of WHO, Dr. Halfdan Mahler, proclaimed a “social revolution in public health” [1]. He saw the establishment of the Special Programme for Research and Training in Tropical Diseases (TDR) Programme in 1975 as playing an active part in the emerging social and health development agenda. In TDR's first public document, Mahler, and his UNDP counterpart Morse stated: “Many millions of the people living in the tropical regions of the world are cut off from the mainstream of social and economic progress. Victims of a heavy burden of disease as well as of harsh economic circumstances, they are not free to choose and plan a better future… Health and development are therefore inextricably interlinked and any strategy for improvement must be based upon this reality” [2]. In these early years of “international” health, it became increasingly apparent that the development of new health technologies was only one component of improved healthcare. Research was required to support the delivery of those technologies, as well as to understand the barriers that could prevent or limit access to them. TDR was one of the first United Nations co-sponsored programmes to recognize this need. From 1977 onward, it had a dedicated focus on applied social and economic research and produced a body of evidence that promoted and expanded the theoretical underpinnings of this field, supported training in low- and middle-income countries where the needs existed, and developed a research agenda and funding to specifically address the “social and economic factors that affect the transmission and control of disease… and to increase the effectiveness of disease control programmes by integrating human behavioural (social, cultural, and economic) factors in programme conception, design, and management” [3], [4]. In its first phase, research investigated human behaviour related to disease exposure, disease-related beliefs and attitudes, attitudes to disease control, the social and economic impact of tropical diseases, costs and effectiveness of alternative disease control measures, and economic development and disease [5]. It focused on the development of theory and methods for applying the social sciences in public health, community participation in disease control, and the economics of tropical diseases [6]. A later emphasis on the “upstream” factors in infectious diseases and their control, from 2000 to 2007, drew from basic social sciences and their subdisciplines, e.g., (medical) anthropology, (health) economics, (medical) sociology, political sciences, and health policy research [7]. Research focused on equity and access, health sector reform, community participation, gender-sensitive interventions [8], [9], globalization, human rights [10], and ethics [11], [12]. TDR also defined and exemplified the concept of applied social sciences for public health (ASSPH) [13], [14] and called for increasing the numbers of scientists trained in these areas. It identified implementation research as a critical aid to help disease control programmes, citing an “absence of capacity and understanding in how to engage with communities and ensure their participation, and of the ability to adapt research methods and health technologies to local contexts,” concluding that “the uptake, effectiveness, and sustainability of these interventions remains limited” [15]. Later, TDR led efforts to define and establish operational and implementation research as scholarly fields [16], [17]. Operational research was defined as “research into strategies, interventions, tools, or knowledge that can enhance the quality, coverage, effectiveness, or performance” of health systems or disease programmes [18]. Implementation research pursues research into the delivery of efficient, sustainable, and effective services; appropriate structure of health systems; the policy process; and other components that are necessary to bring new and old control interventions into the routine practice of national health systems and improve access particularly for vulnerable population groups. Both types of research are complementary [19].

Frequent wildfires increasingly impact tourist populations, yet there is a shortage of evidence-based, human-centered tools for wildfire risk reduction tailored to these areas. Most current tools focus primarily on assessing and reducing physical vulnerabilities, overlooking human aspects. While some community wildfire management guidelines exist, actionable strategies for disaster managers to address tourist-specific vulnerabilities are absent. This study aligned with existing vulnerability assessment methodologies, utilizing qualitative interviews, site visits, and literature review to identify key characteristics of tourist vulnerabilities and develop effective mitigation strategies. As a result, we developed TOURSAFE—a freely accessible tool for disaster risk managers in tourist areas. Based on human behavior in fire scenarios, expected evacuation decisions, and key actors’ expertise, TOURSAFE assists in identifying tourism-related wildfire vulnerabilities and offers relevant, adaptable mitigation strategies. This tool is easy to use, accessible, and provides actionable advice for short-, medium-, and long-term planning.

Predicting human behavior presents significant challenges in various domains. Chapter 1 adopts a novel methodological approach, utilizing neurophysiological responses measured and utilized machine learning to predict hit songs. Statistical analyses reveal the effectiveness of machine learning techniques, showcasing a 97% accuracy in classifying hit songs. This underscores the potential of machine learning applied to neural data in enhancing market outcome predictions. Chapter 2 focuses on the proactive assessment of mood among the elderly using continuous neurophysiological data. By integrating self-reports with neurophysiological measures, the study demonstrates a high predictive accuracy in identifying low mood and energy levels. Applications of such predictions can enable timely interventions to mitigate the risk of depression in vulnerable populations. In Chapter 3, a proactive approach to mental health assessment is explored through the continuous collection of neurophysiological data in the same population as chapter 2. Statistical analyses unveil the predictive power of neurophysiological troughs and peaks in determining daily mood fluctuations, with machine learning models achieving 90% accuracy. The study underscores the viability of commercially available neuroscientific technologies in assessing mood disorders and promoting well-being. Collectively, this dissertation underscores the transformative potential of neuroscientific methodologies in predicting human behavior and experience. By leveraging machine learning and continuous neurophysiological monitoring, these studies offer insights into proactive interventions and personalized approaches to improve societal well-being and prevent mental health crises.

Death is a common existential concern, and fear of death is widespread at subclinical levels. Near-death experiences (NDEs) are profound mystical experiences that may occur close to death, and they are usually followed by dramatic reductions in fear of death. The content of these experiences varies in each case, but NDEs tend to have common features among individuals and across cultures. While many studies using different tools to measure fear of death and death anxiety have documented their reduction after NDEs, we do not know the influence of specific NDE features on this effect. This research presents statistical correlations between features of NDEs and post-experience death attitudes, both negative and positive. We found that encountering mystical beings and having a life review during one’s NDE are the strongest predictors of reduced fear of death. Contrary to theoretical predictions, a sense of disembodiment is not associated with change in death attitudes. These findings can be used to design interventions aimed at reducing fear of death in vulnerable populations, including people at end-of-life. We also discuss these findings in the context of Terror Management Theory regarding death awareness as a motivator of human behaviour.

Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology-related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology-related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology-related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology-related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.

2E.003 Burden of suicide in Nepal: an analysis of police records