JMIR Public Health and Surveillance

The triglyceride-glucose (TyG) index and blood pressure (BP) status are key indicators associated with coronary heart disease (CHD). While limited research has focused on individuals with disabilities.

Condyloma acuminata (CA), the clinical manifestation of infection with low-risk human papillomaviruses (HPV 6 and 11), is a common sexually transmitted infection (STI) with recurrent lesions and notable psychosocial and health system burden. Recent evidence indicates a substantial global burden, with prevalence ranging from 0.5% to 33.1% and incidence from 24 to 2940 per 100,000 person-years, varying by age, sex, time, and geography. In South Korea, national insurance data show sustained increases in patients receiving care for CA during 2010 to 2019. Beyond individual behaviors, spatial proximity and contextual factors can produce clustered STI risk. However, the municipal-level spatial distribution of CA in Korea and its contextual correlates remain understudied.

Chronic kidney disease and end-stage renal disease are major contributors to the disease burden in low- and middle-income countries, including Indonesia. Despite the expansion of universal health coverage through Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan, Indonesia’s national health insurance program, disparities in access to hemodialysis persist across different socioeconomic and geographic groups. Understanding these inequities is critical to advancing equitable health care access.

Interpersonal violence (IV) has an extensive and profound impact on health, representing a public health concern. Different health outcomes have been identified based on the characteristics of the survivor and the abuser, their relationship, the type of violence perpetrated, and the cumulative effect of multiple violent experiences.

Despite the proven efficacy of pre-exposure prophylaxis (PrEP) in reducing the risk of HIV transmission, uptake remains suboptimal among populations with limited access and availability to PrEP-prescribing locations, particularly in the Southern United States. The accessibility of pharmacies positions them as a promising resource for expanding PrEP delivery and access and supporting uptake and adherence through HIV prevention programs to reduce geographic disparities.

Timely surveillance of diabetes mellitus remains a challenge for public health agencies. In this study, researchers compared Type 2 Diabetes (T2D) prevalence estimates using electronic health record (EHR) data and computable phenotypes (CPs) as defined and applied by two independent networks. One network, Assessing the Burden of Diabetes by Type in Children, Adolescents, and Young Adults (DiCAYA) was a research consortium, and the other, the Multi-State EHR-Based Network for Disease Surveillance (MENDS), was designed to be a practice-based public health surveillance network.

Electronic healthcare databases are widely used for epidemiological studies. However, they may contain inactive records of individuals no longer participating in the healthcare system. These inactive records create a methodological challenge as they systematically appear as unexposed with no recorded outcomes. Given the widespread healthcare system engagement during the COVID-19 pandemic, the English National Health Service (NHS) which hosts a national pandemic planning and research dataset, with linkage to COVID-19 vaccination and emergency care data make it an ideal setting to identify the extent of overrepresentation due to inactive healthcare records, and assess ways to mitigate them.

Machine learning (ML), a subset of artificial intelligence, uses large datasets to identify patterns between potential predictors and outcomes. ML involves iterative learning from data and is increasingly used in population and public health. Examples include early warning of infectious disease outbreaks, predicting the future burden of non-communicable diseases, and assessing public health interventions. However, ML can inadvertently produce biased outputs related to the quality and quantity of data, who is engaged and helping direct the analysis, and how findings are interpreted. Specific guidelines for using ML in population and public health have not yet been created. We assembled a diverse team of experts in computer science, statistical modeling, clinical and population health epidemiology, health economics, ethics, sociology, and public health. Drawing on literature reviews and a modified Delphi process, we identified five key recommendations: (1) prioritize partnerships and interventions to support structurally disadvantaged communities; (2) use ML for dynamic situations, such as public health emergencies, while adhering to ethical standards; (3) conduct risk assessments and bias mitigation strategies aligned with identified risks; (4) ensure technical transparency and reproducibility by publicly sharing data sources and methodologies; (5) foster multidisciplinary dialogue to discuss the potential harms of ML-related bias and raise awareness among the public and public health community. The proposed guidelines provide operational steps for stakeholders, ensuring that ML tools are not only effective but also ethically grounded and feasible in real-world scenarios.

A particular challenge during the Covid-19 pandemic was to provide testing and treatment for already disadvantaged and vulnerable populations. Many states implemented testing in a sporadic and disorganized way, and it is unclear to what extent this disproportionally affected populations experienced barriers to accessing care. It is also unclear that if potential barriers to testing were due to systemic challenges, such as rurality, or whether there were underlying individuals’ motivations for not getting tested.

The major drawback of a cohort study design is the loss to follow-up, which increases selection bias and threatens external validity, particularly in online surveys. It is important to identify factors beyond population or demographics that influence nonresponse rates in cohort studies.

Ultraprocessed food (UPF) consumption is very common among adolescents. Previous studies have suggested an association between UPF consumption and adolescent mental health problems, but studies on multiethnic adolescents in China are rare.

Human papillomavirus (HPV) is a primary causative agent of cervical cancer, accounting for over 90% of cases worldwide. Epidemiological data on regional HPV prevalence and genotype distribution are critical for tailoring targeted cervical cancer prevention strategies, particularly in regions with limited population-based studies.