JMIR Public Health and Surveillance
A multidisciplinary journal that focuses on the intersection of public health and technology, public health informatics, mass media campaigns, surveillance, participatory epidemiology, and innovation in public health practice and research.
Editor-in-Chief:
Travis Sanchez, DVM, MPH, Emory University Rollins School of Public Health, USA
Impact Factor 3.9 More information about Impact Factor CiteScore 6.3 More information about CiteScore
Recent Articles



Although recovery is a central tenet of the US substance use disorder service delivery system, empirical research on youth recovery remains limited and underdeveloped. Notably, no population-based representative surveys, either in the United States or internationally, currently assess recovery status among secondary school–aged youth (aged 14-18 years). Consequently, little is known about how many youth identify as being in recovery or about their characteristics and needs.

Antimicrobial resistance is a public health crisis exacerbated by the irrational use of antibiotics, particularly in low- and middle-income countries. Pakistan, one of the highest consumers of antibiotics globally, faces unique challenges, including unregulated sales, overuse of broad-spectrum antibiotics, and inadequate stewardship programs.

Adherence to antiretroviral therapy (ART) during pregnancy is critical for maternal health and the prevention of vertical HIV transmission. In Uganda, where HIV prevalence remains high, pregnant women living with HIV face intersecting structural and psychosocial challenges, including stigma, food insecurity, and limited social support. Although each factor has been linked to ART nonadherence, less is known about how these factors co-occur within individuals and jointly shape vulnerability to nonadherence during pregnancy.

Everyday digital technologies such as social media, gaming, and internet use are deeply integrated into the lives of children, adolescents, and young adults. While these platforms can foster connection, learning, and entertainment, concerns have grown about their potential to influence mental, physical, and social well-being. Research on this topic has expanded rapidly over the past decade, yet much of it remains cross-sectional, limiting insights into long-term outcomes. Longitudinal studies are essential to capture evolving patterns of digital engagement, identify causal relationships, and guide effective policies and interventions that support youth in navigating digital environments. In particular, evidence is needed to distinguish between beneficial and harmful forms of digital engagement, such as social connection versus problematic use, and to understand how these impacts differ across diverse populations and contexts. The COVID-19 pandemic further accelerated young people’s technology use, underscoring the urgency of examining both risks and opportunities. This review, therefore, synthesizes longitudinal research to map trends, identify knowledge gaps, and inform future directions.

Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.


Modifiable unhealthy behaviors account for over two-thirds of new cases of noncommunicable diseases. Behavioral risk factor reduction is a potentially cost-effective means to improve long-term health outcomes. Although family serves as a pivotal cornerstone for fostering and maintaining individuals’ health, the associations between family health (FH) and the proactive health risk management index (PHRMI) remain unclear.

Acute respiratory infections (ARIs) remain a significant global health challenge and are the second leading cause of disease burden and mortality. Early warning systems (EWS) play a key role in detecting clinical deterioration, alerting health care providers (HCPs), and supporting pandemic surveillance. While existing literature highlights HCPs’ positive experiences with EWS in confirming clinical assessments and guiding escalation, perspectives on how these systems can be optimized for ARI management remain underexplored.

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