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Social determinants of health strongly influence clinical outcomes. Social needs are the individual-level, actionable facets of the broader social determinants of health framework, including food security, stable housing, and access to essential services. When these needs go unmet, they adversely affect well-being and quality of care. Systematically detecting social needs is therefore critical, and emerging digital tools now offer efficient, scalable approaches for screening and identification. This scoping review aimed to examine the use of digital health technology (DHT) or DHT-based interventions documented for screening and identifying unmet social needs in populations with high needs. We explore trends, effects, challenges, and limitations associated with these technologies. Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched databases including MEDLINE, Embase, Scopus, ACM Digital Library, and Web of Science for studies published from 2010 to 2025. Eligible studies used technology to screen for and identify unmet social needs in populations with health and socioeconomic challenges. Data extraction focused on the types of technology, screening processes, and social needs identified. Our findings highlight a limited yet evolving landscape of technological applications. We identified 14 studies using tools such as self-assessment surveys, tablet-based systems, and electronic portals. These tools were applied across diverse groups, such as refugees and patients in emergency departments. Innovative approaches, such as chatbots and multidimensional risk appraisal systems for older adults, showed potential. However, challenges included single-site studies, small samples, and integration issues with medical records. The effectiveness of these tools in screening for unmet social needs shows mixed outcomes. DHTs play a pivotal role in improving the identification of unmet social needs. The findings underscore the need for broader, more integrated research to fully understand the impact of technology-based assessments and screening processes for social needs. Future efforts should focus on facilitated screening using technology both within and outside of the visit, ensuring the linkage to appropriate resources and care.

The adoption of new technologies and data modernization approaches in public health aims to enhance the use of health data to inform decision-making and improve population health. However, public health departments struggle with legacy systems, siloed data, and privacy concerns, which hamper the adoption of new technology and data sharing with stakeholders. This paper maps how to address these shortcomings by identifying data modernization challenges, initiatives, and progress. This study aims to characterize evidence for data modernization-associated gaps and best practices in public health. This scoping review was conducted using the 5-stage framework developed by Arksey and O'Malley and was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A structured search was performed in databases PubMed, Scopus, CINAHL, and PsycINFO, and was complemented by a further search in the Google Scholar search engine, covering publications from January 1, 2019, to April 30, 2024. Eligible studies were peer-reviewed, published in English, and focused on data modernization initiatives within US public health system and reported on best practices, challenges, and outcomes. Search terms combined concepts such as \"Data Modernization,\" \"Interoperability,\" and \"Public Health\" using Boolean operators. Two reviewers independently screened titles, abstracts, and full texts using Rayyan QCRI, with conflicts resolved through consultation with a third reviewer. Data were extracted into Microsoft Excel and thematically analyzed. This review analyzed 21 studies focused on public health data modernization. Across the literature, common components included transitioning to cloud-based systems, consolidating fragmented data into unified platforms, applying governance frameworks, and implementing analytics tools to support decision-making. Primary data sources were electronic health records, insurance claims, and disease surveillance registries. Key challenges identified across studies involved data quality issues, lack of interoperability, and limited resources, particularly in underfunded settings. Notable benefits included more timely and accessible data, improved integration across systems, and enhanced analytical capabilities, which collectively support more responsive and effective public health interventions when guided by clear standards and policy alignment. Progress hinges on balancing local adaptability with national coordination, improving data governance practices, and enhancing collaboration across institutions. These steps are vital to ensure that public health systems can deliver timely, accurate, and actionable information to support effective public health efforts.

Social media platforms have become influential spaces for disseminating information about electronic cigarettes (e-cigarettes). Concerns persist about the spread of misleading content, particularly among social media vulnerable groups. Xiaohongshu (RedNote), widely used by Chinese youth, plays a growing role in shaping e-cigarette perceptions. Understanding the narratives circulating on this platform is essential for identifying misinformation, assessing public perception, and guiding future health communication strategies. This study aimed to analyze the content, topics, user engagement, and sentiment trends of e-cigarette-related posts on Xiaohongshu and to assess the factors that influence engagement. E-cigarette-related posts published on Xiaohongshu between January 2020 and November 2024 were collected using web scraping, based on a predefined keyword list and a time-stratified random sampling strategy. Posts were categorized into 4 themes: advertising promotion, health hazards, usage interaction, and others. High-frequency keywords were extracted, and representative quotes were included to illustrate user perspectives across each category. Sentiment analysis was performed on posts in the usage interaction category to assess public attitudes. We defined 4 sentiment categories: positive, negative, neutral, and mixed. Logistic regression was conducted to explore the effects of post type, content length, and thematic classification on user engagement metrics such as likes, saves, and comments. A total of 1729 posts were included and analyzed. Usage interaction posts were the most common (681/1729, 39.39%), with keywords such as \"experience,\" \"regulations,\" and \"quit smoking\" dominating this category. Advertising promotion posts (512/1729, 29.61%) frequently used terms like \"flavor,\" \"fashion,\" and \"design\" to attract younger users. Health hazards posts (311/1729, 17.99%) highlighted risks with keywords like \"nicotine,\" \"addiction,\" and \"secondhand smoke,\" while others included policy and industry updates. Representative quotes highlighted typical concerns about aesthetics, health risks, and cessation struggles. Health hazards posts garnered the highest engagement in terms of likes and saves, despite their limited presence (odds ratio [OR] 1.498, 95% CI 1.099-2.042, P=.01). Video posts significantly outperformed text-image posts in generating comments (OR 2.624, 95% CI 2.017-3.439, P<.001). Sentiment analysis of the usage interaction posts (n=681) revealed that 53.45% (364/681) were positive, highlighting reduced harm, convenience, or flavor preferences. Negative sentiment was observed in 33.48% (228/681) of posts, often expressing concerns about addiction and health risks. Mixed sentiments appeared in 6.90% (47/681), acknowledging both pros and cons. In addition, 6.17% (42/681) of posts were classified as neutral without evident emotional tone. The findings underscore the dual role of Xiaohongshu as a platform for both e-cigarette promotion and public discourse. Misleading marketing targeting vulnerable groups, such as adolescents, remains a critical issue. However, the strong user response to health-related content suggests that social media platforms could be leveraged for effective health education. Strengthened regulatory oversight and educational campaigns leveraging engaging content formats are urgently needed to counter misinformation and protect public health.

HIV remains a global challenge, with stigma, financial constraints, and psychosocial barriers preventing people living with HIV from accessing health care services, driving them to seek information and support on social media. Despite the growing role of digital platforms in health communication, existing research often narrowly focuses on specific HIV-related topics rather than offering a broader landscape of thematic patterns. In addition, much of the existing research lacks large-scale analysis and predominantly predates COVID-19 and the platform's transition to X (formerly known as Twitter), limiting our understanding of the comprehensive, dynamic, and postpandemic HIV-related discourse. This study aims to (1) observe the dominant themes in current HIV-related social media discourse, (2) explore similarities and differences between theory-driven (eg, literature-informed predetermined categories) and data-driven themes (eg, unsupervised Latent Dirichlet Allocation [LDA] without previous categorization), and (3) examine how emotional responses and temporal patterns influence the dissemination of HIV-related content. We analyzed 191,972 tweets collected between June 2023 and August 2024 using an integrated analytical framework. This approach combined: (1) supervised machine learning for text classification, (2) comparative topic modeling with both theory-driven and data-driven LDA to identify thematic patterns, (3) sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) and the NRC Emotion Lexicon to examine emotional dimensions, and (4) temporal trend analysis to track engagement patterns. Theory-driven themes revealed that information and education content constituted the majority of HIV-related discourse (120,985/191,972, 63.02%), followed by opinions and commentary (23,863/191,972, 12.43%), and personal experiences and stories (19,672/191,972, 10.25%). The data-driven approach identified 8 distinct themes, some of which shared similarities with aspects from the theory-driven approach, while others were unique. Temporal analysis revealed 2 different engagement patterns: official awareness campaigns like World AIDS Day generated delayed peak engagement through top-down information sharing, while community-driven events like National HIV Testing Day showed immediate user engagement through peer-to-peer interactions. HIV-related social media discourse on X reflects the dominance of informational content, the emergence of prevention as a distinct thematic focus, and the varying effectiveness of different timing patterns in HIV-related messaging. These findings suggest that effective HIV communication strategies can integrate medical information with community perspectives, maintain balanced content focus, and strategically time messages to maximize engagement. These insights provide valuable guidance for developing digital outreach strategies that better connect healthcare services with vulnerable populations in the post-COVID-19 pandemic era.

The DC Cohort is a longitudinal HIV cohort study of people with HIV receiving care at 14 clinical sites in Washington, DC, led by George Washington University. Data are routinely linked to the DC Department of Health (DC Health) HIV surveillance databases to increase data completeness and accuracy and to help identify people with HIV enrolled at multiple sites. The ATra Black Box (Black Box henceforth) is a novel privacy technology developed by Georgetown University, which is currently deployed in 40 public health jurisdictions. The Black Box provides a secure mechanism to link private health information across data systems. The Black Box was modified for the purposes of linking data from the DC Cohort to DC Health surveillance data and increasing the ease, feasibility, accuracy, and timeliness of future linkages. These modifications included providing deidentified data to George Washington University and developing analytic code to compare data between the DC Cohort and DC Health to report on data discrepancies. This paper reports on the results of the initial linkage using the Black Box. DC Cohort data on all consented participants from January 2011 through September 2022 were submitted to the Black Box. Simultaneously, all DC Health HIV surveillance data were also submitted to the Black Box. The data were matched using a predetermined algorithm, match-level scores were assigned, and matches were manually verified. The new Black Box graphical user interface allows users to check files for errors and easily track the Black Box processes and provides analytic plugins for running SAS code. A total of 9744 records of DC Cohort participants were submitted for matching to DC Health. Of these, 9060 participants (93.0%) matched to surveillance data and were validated through manual review. Match-level scores ranged from 20 to 100, and the validation found that scores of 61 and above were true matches. The SAS output files provided information on missing or conflicting data, including lab records, date of HIV diagnosis, and other key demographics. The linkage resulted in the addition of 48,970 CD4 T-lymphocyte counts, 33,413 viral load lab records, and 767 previously unrecognized deaths. Among the DC Cohort participants, 470 were enrolled at more than one site and 17 at more than two sites. The implementation of the Black Box for sharing DC Cohort and DC Health data resulted in better capture of HIV lab records, improved vital status information, and enhanced characterization of care patterns for people with HIV enrolled in the DC Cohort. Future linkages will include DC Health data on diagnoses of sexually transmitted infections, hepatitis, and tuberculosis.

Public health surveillance systems are critical for decision-making and have been advanced by monitoring infectious diseases. This study aims to assess the effectiveness and timeliness of multiple surveillance systems in tracking COVID-19 cases in the postpandemic era. Data of COVID-19-reported cases in a southern city of China were collected from the National Notifiable Disease Reporting Information System over a 1-year period, following the easing of the COVID-19 pandemic restrictions (from April 1, 2023, to June 30, 2024) as the operational benchmark. A total of 4 surveillance systems (hospital, wastewater, meteorological, and internet search engine) were integrated into a daily time series. Spearman correlation and 60-day moving window analyses with 7-day lags were used to assess associations. Distributed lag nonlinear models captured nonlinear meteorological effects. Time-series regression models assessed lead effects (0-7 d) of each surveillance indicator, with and without meteorological adjustment. Among 4 surveillance systems, 16 variables correlated significantly with reported cases. The nucleic acid amplification test (NAAT) positivity rate showed the strongest correlation, with a coefficient of 0.834 (95% CI 0.803-0.860). Wastewater surveillance system demonstrated a moderate correlation, with the correlation coefficient of 0.776 (95% CI 0.737-0.810) for the N gene positivity rate and 0.698 (95% CI 0.648-0.743) for the N gene concentration. Moving-window analyses confirmed a stable correlation between NAAT positivity and reported cases (median 0.534, IQR 0.394-0.724; 58% of windows ρ>0.5), while wastewater indicators exhibited greater temporal fluctuation, with the N gene concentration (median 0.585, IQR 0.214-0.766; 60.8% of windows ρ>0.5) exceeding the N gene positivity rate (median 0.530, IQR 0.222-0.742; 53.5% of windows ρ>0.5). Time-series analysis identified same-day associations (lag 0) for both NAAT positivity (β=.819, 95% CI 0.768-0.870) and wastewater signals (maximum effect: β=1.023, 95% CI 0.931-1.115). Meteorological factors significantly modified the effect of internet surveillance indicators (P<.05), particularly temperature and absolute humidity. An integrated, multichannel surveillance strategy of leveraging wastewater, clinical, and digital streams with meteorological contextualization can strengthen early warning and situational awareness for respiratory pathogen threats.

Recruitment to population-based health studies remains challenging, with difficulties meeting target participant numbers, biosample returns, and achieving a representative sample. Few studies provide evaluations of traditional and web-based recruitment methods particularly for studies with broad inclusion criteria and extended recruitment periods. Generation Scotland (GS) is a family-based cohort study that initiated a new wave of recruitment in 2022 using web-based data collection and remote saliva sampling (for genotyping). Here, we provide an overview of recruitment strategies used by GS over the first 18 months of new recruitment, highlighting which proved most effective and cost-efficient in order to inform future research. This study evaluated recruitment strategies using four main outcomes: (1) absolute recruitment numbers, (2) sociodemographic representativeness, (3) biosample return rate, and (4) cost per participant. Between May 2022 and December 2023, recruitment was undertaken via snowball recruitment (through friends and family of existing volunteers), invitations to those who participated in a previous survey (CovidLife: the GS COVID-19 impact survey), and Scotland-wide recruitment through social media (including sponsored Meta-advertisements), news media, and TV advertisement. The method of recruitment was self-reported in the baseline questionnaire. We present absolute recruitment numbers and sociodemographic characteristics by recruitment method and evaluate the saliva sample return rate by recruitment strategy using chi-square tests. The overall cost and cost per participant were calculated for each method. In total, 7889 new participants joined the cohort over this period. Recruitment sources by contribution were social media (n=2436, 30.9%), survey responder invitations (n=2049, 26.0%), TV advertising (n=367, 17.3%), snowball (n=891, 11.3%), news media (n=747, 9.5%), and other methods or unknown (n=399, 5.0%). More females signed up than males (5570/7889, 70.5% female). To date, 83.5% (6543/7836) of participants returned their postal saliva sample, which also varied by demographic factors (3485/3851, 90.5% older than 60 years vs 471/662, 71.1% aged 16-34 years). Average cost per participant across all recruitment strategies was £13.52 (US $16.82). Previous survey recontacting was the most cost-effective (£0.37 [US $0.46]), followed by social media (£14.78 [US $18.39]), while TV advertisement recruitment was the most expensive per recruit (£33.67 [US $41.89]). This study highlights both the challenges and the opportunities in large web-based cohort recruitment. Overall, social media advertising has been the most cost-effective and easily sustained strategy for recruitment over the reported recruitment period. We note that different strategies resulted in successful recruitment over varying timescales (eg, consistent sustained recruitment for social media and large spikes for news media and TV advertising), which may be informative for future studies with different requirements of recruitment periods. Limitations include self-reported methods of recruitment and difficulties in evaluating multilayered recruitment. Overall, these data demonstrate the potential cost requirements and effectiveness of different strategies that could be applied to future research studies.

The rapid integration of information technology into daily life has exacerbated the digital divide (DD), particularly among older adults, who often face barriers to technology adoption. Although prior research has linked technology use to cognitive benefits, the long-term neurostructural and cognitive consequences of the DD remain poorly understood. The aim of this study is to use large-scale neuroimaging data to examine how the DD affects long-term brain structure and cognitive aging in older adults. It specifically investigates (1) structural and cognitive differences between older adults with and without DD engagement, (2) predictive relationships between group-distinctive brain regions and cognitive outcomes, and (3) longitudinal impacts of DD exposure on accelerated aging trajectories of neural substrates and cognitive functions. The study included 1280 community-dwelling older adults (aged 65-90 y) who completed comprehensive cognitive assessments and structural magnetic resonance imaging scans at baseline. Longitudinal data were available for 689 participants (mean follow-up 3.2 y). Participants were classified into the DD (n=640) and overcoming DD (n=640) groups using rigorous propensity score matching to control for age, education, gender, and baseline health conditions. A computational framework using the searchlight technique and cross-validation classification model investigated group differences in structural features and cognitive representation. The aging rate of each voxel's structural feature was calculated to explore the long-term influence of the DD. The DD group showed significant deficits in executive function (t=4.75; P<.001; Cohen d=0.38) and processing speed (t=4.62; P<.001; Cohen d=0.37) compared to the overcoming DD group. Reduced gray matter volume in the DD group spanned the fusiform gyrus, hippocampus, parahippocampal gyrus, and superior temporal sulcus (false discovery rate-corrected P<.05). The computational framework identified the key structural substrates related to executive function and processing speed, excluding the ventro-orbitofrontal lobe (classification accuracy <0.6). Longitudinal findings highlighted the long-term impact of the DD. The DD group exhibited faster gray matter volume decline in the middle frontal gyrus (t=3.95 for the peak voxel in this cluster, false discovery rate-corrected P<.05), which mediated 17% of episodic memory decline (P=.02). Older adults who overcome the DD demonstrate preserved gray matter structure and slower cognitive decline, particularly in frontotemporal regions critical for executive function. Our findings underscore that mobile digital interventions should be explored as potential cognitive decline prevention strategies.

The growing ubiquity of digital footprint data presents new opportunities for behavioral epidemiology and public health research. Among these, supermarket loyalty card data—passively collected records of consumer purchases—offer objective, high-frequency insights into health-related behaviors at both individual and population levels. This paper explores the potential of loyalty card data to strengthen public health surveillance across 4 key behavioral risk domains: diet, alcohol, tobacco, and over-the-counter medication use. Drawing on recent empirical studies, we outline how these data can complement traditional epidemiological data sources by improving exposure assessment, enabling real-time trend monitoring, and supporting intervention evaluation. We also discuss critical methodological challenges, including issues of representativeness, data integration, and privacy, as well as the need for robust validation strategies. By synthesizing the current evidence base and offering practical recommendations for researchers, this paper highlights how loyalty card data can be responsibly leveraged to advance behavioral risk monitoring and support the adaptation of epidemiological practice to contemporary digital data environments.

The majority of global maternal and newborn deaths occur in low- and middle-income countries (LMICs), often due to a lack of resources, inadequate training of health care providers, and delayed or untimely care. Low-cost digital health interventions (DHIs) may help improve emergency obstetric and newborn care (EmONC) services in resource-limited settings by incorporating innovative approaches to enhance traditional models of care. This study aimed to systematically explore the key characteristics and usefulness of DHIs implemented for improving EmONC services in low-resource settings, as well as to identify barriers to implementation, given the importance of developing, implementing, and evaluating context-specific digital interventions for such settings. We followed the existing guidelines for conducting this scoping review, including the methodological framework for scoping studies, the updated Joanna Briggs Institute Methodology for Scoping Review, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched 3 databases-PubMed, Web of Science, and the Cochrane Library-and identified studies published before November 2024 that described digital interventions aimed at enhancing EmONC in LMICs. Extracted data included the following: purposes, features, and functionalities of DHIs, mode of delivery, outcomes, and barriers to implementation. We used the Mixed Methods Appraisal Tool for assessing study quality. A total of 33 eligible studies from 18 countries were included in the review that described 21 distinct DHIs. Most qualitative (7/8) and mixed methods studies (4/5) were of high quality. However, most quantitative descriptive studies (15/20) had some form of sampling issues. The digital interventions were reported either as standalone interventions (n=19) or combined with other nondigital approaches (n=13). Most studies used mobile health-based interventions, primarily targeting health care providers (n=28) through mobile apps and text-based messaging, with a focus on EmONC education and training (n=19). The review's findings suggest generally positive impacts of DHIs on health care providers' clinical practices, although maternal and perinatal health outcomes varied depending on the type of intervention. Although DHIs have the potential to improve services and access to EmONC in various health care settings, the advancement and implementation of these technologies in LMICs have progressed at a slow pace. The most common barrier identified was the lack of EmONC resources such as medication, skilled workforce, and ambulances, which challenged the implementation of these interventions. Our findings highlight the potential of DHIs to improve EmONC services in resource-scarce settings. Future research is needed in this area, which should prioritize the rigorous evaluation of DHIs, focusing on maternal and perinatal health outcomes, addressing context-specific challenges in health infrastructure, and evaluating the cost-effectiveness to support the development, effective use, and regulation of DHIs in LMICs. The proposed framework, based on our findings, can be used as a guide to develop and implement DHIs for EmONC support in low-resource settings.