Precision public health, the key for future outbreak management: A scoping review.

Background: Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed. Objective: We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach. Methods and analysis: Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative. Results: A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%). Conclusions: PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.

Introduction: The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy. Methods: A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method. Results: Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity. Discussion: While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

Public health genomics research in Italy: an overview of ongoing projects.

Public health genomics (PHG) aims to integrate advances in genomic sciences into healthcare for the benefit of the general population. As in many countries, there are various research initiatives in this field in Italy, but a clear picture of the national research portfolio has never been sketched. Thus, we aimed to provide an overview of current PHG research projects at the national or international level by consultation with Italian institutional and academic experts. We included 68 PHG projects: the majority were international projects in which Italian researchers participated (n = 43), mainly funded by the European Commission, while the remainder were national initiatives (N = 25), mainly funded by central government. Funding varied considerably, from € 50,000 to € 80,803,177. Three main research themes were identified: governance (N = 20); precision medicine (PM; N = 46); and precision public health (N = 2). We found that research activities are preferentially aimed at the clinical application of PM, while other efforts deal with the governance of the complex translation of genomic innovation into clinical and public health practice. To align such activities with national and international priorities, the development of an updated research agenda for PHG is needed.

Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

BACKGROUND: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). OBJECTIVE: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. METHODS: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. RESULTS: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. CONCLUSIONS: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

Completion of the Transfer of the Online Journal of Public Health Informatics (OJPHI) to JMIR Publications.

Founded in 2009, the Online Journal of Public Health Informatics (OJPHI) strives to provide an unparalleled experience as the platform of choice to advance public and population health informatics. As a premier peer-reviewed journal in this field, OJPHI's mission is to serve as an advocate for the discipline through the dissemination of public health informatics research results and best practices among practitioners, researchers, policymakers, and educators. However, in the current environment, running an independent open access journal has not been without challenges. Judging from the low geographic spread of our current stakeholders, the overreliance on a small volunteer management staff, the limited scope of topics published by the journal, and the long article turnaround time, it is obvious that OJPHI requires a change in direction in order to fully achieve its mission. Fortunately, our new publisher JMIR Publications is the leading brand in this field, with a portfolio of top peer-reviewed journals covering innovation, technology, digital medicine and health services research in the internet age. Under the leadership of JMIR Publications, OJPHI plans to expand its scope to include new topics such as precision public health informatics, the use of artificial intelligence and machine learning in public health research and practice, and infodemiology in public health informatics.

An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI).

BACKGROUND: Most COVID-19 vulnerability indices rely on measures that are biased by rates of exposure or are retrospective like mortality rates that offer little opportunity for intervention. The Moore-Hill Vulnerability Index (MHVI) is a precision public health early warning alternative to traditional infection fatality rates that presents avenues for mortality prevention. METHODS: We produced an infection-severity vulnerability index by calculating the proportion of all recorded positive cases that were severe and attended by ambulances at small area scale for the East Midlands of the UK between May 2020 and April 2022. We produced maps identifying regions with high and low vulnerability, investigated the accuracy of the index over shorter and longer time periods, and explored the utility of the MHVI compared to other common proxy measures and indices. Analysis included exploring the correlation between our novel index and the Index of Multiple Deprivation (IMD). RESULTS: The MHVI captures geospatial dynamics that single metrics alone often overlook, including the compound health challenges associated with disadvantaged and declining coastal towns inhabited by communities with post-industrial health legacies. A moderate negative correlation between MHVI and IMD reflects spatial analysis which suggests that high vulnerability occurs in affluent rural as well as deprived coastal and urban communities. Further, the MHVI estimates of severity rates are comparable to infection fatality rates for COVID-19. CONCLUSIONS: The MHVI identifies regions with known high rates of poor health outcomes prior to the pandemic that case rates or mortality rates alone fail to identify. Pre-hospital early warning measures could be utilised to prevent mortality during a novel pandemic.

Calibrating variant curation by clinical context based on factors that influence patients' tolerance of uncertainty.

PURPOSE: Shared decision making manages genomic uncertainty by integrating molecular and clinical uncertainties with patient values to craft a person-centered management plan. Laboratories seek genomic report consistency, agnostic to clinical context. Molecular reports often mask laboratory-managed uncertainties from clinical decision making. Better integration of these uncertainty management strategies requires a nuanced understanding of patients' perceptions and reactions to test uncertainties. We explored patients' tolerance to variant uncertainty in 3 parameters: (1) relative causal significance, (2) risk accuracy, and (3) classification validity. METHOD: Deliberative forums were undertaken with 18 patients with predictive testing experience. Uncertainty deliberations were elicited for each parameter. A thematic framework was first developed, and then mapped to whether they justified tolerance to more or less parameter-specific uncertainty. RESULTS: Six identified themes mapped to clinical and personal domains. These domains generated opposing forces when calibrating uncertainty. Personal themes justified tolerance of higher uncertainty and clinical themes lower uncertainty. Decision making in uncertainty focused on reducing management regret. Open communication increased tolerance of classification validity and risk accuracy uncertainty. Using these data, we have developed a nascent clinical algorithm integrating molecular uncertainty with clinical context through a targeted communication framework. CONCLUSION: Maximizing test utility necessitates context-specific recalibration of uncertainty management and communication.

Precision Nutrition Opportunities to Help Mitigate Nutrition and Health Challenges in Low- and Middle-Income Countries: An Expert Opinion Survey.

Precision nutrition involves several data collection methods and tools that aim to better inform nutritional recommendations and improve dietary intake, nutritional status, and health outcomes. While the benefits of collecting precise data and designing well-informed interventions are vast, it is presently unclear whether precision nutrition is a relevant approach for tackling nutrition challenges facing populations in low- and middle-income countries (LMIC), considering infrastructure, affordability, and accessibility of approaches. The Swiss Food & Nutrition Valley (SFNV) Precision Nutrition for LMIC project working group assessed the relevance of precision nutrition for LMIC by first conducting an expert opinion survey and then hosting a workshop with nutrition leaders who live or work in LMIC. The experts were interviewed to discuss four topics: nutritional problems, current solutions, precision nutrition, and collaboration. Furthermore, the SFNV Precision Nutrition for LMIC Virtual Workshop gathered a wider group of nutrition leaders to further discuss precision nutrition relevance and opportunities. Our study revealed that precision public health nutrition, which has a clear focus on the stratification of at-risk groups, may offer relevant support for nutrition and health issues in LMIC. However, funding, affordability, resources, awareness, training, suitable tools, and safety are essential prerequisites for implementation and to equitably address nutrition challenges in low-resource communities.

Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial.

BACKGROUND: Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities. OBJECTIVE: This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities. METHODS: This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation-related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. RESULTS: Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (ß=.2187; P<.001), with the highest effect on cluster 1 (ß=.3683; P<.001) and the lowest effect on cluster 4 (ß=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS: The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. TRIAL REGISTRATION: ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.

Testing Theory-Based Messages to Encourage Women at Average Risk for Breast Cancer to Consider Biennial Mammography Screening.

BACKGROUND: The US Preventive Services Task Force does not recommend routine annual mammography screening for women aged 40-49 at average risk. Little research has been done to develop theory-based communication interventions to facilitate informed decision-making about reducing potentially low-value mammography screening. PURPOSE: Evaluate the effects of theory-based persuasive messages on women's willingness to consider delaying screening mammography until age 50 or have mammograms biennially. METHODS: We conducted a randomized controlled communication experiment online with a population-based sample of U.S. women aged 40-49 (N = 383) who screened to be at average risk for breast cancer. Women were randomly assigned to the following messaging summaries: annual mammography risks in 40s (Arm 1, n = 124), mammography risks plus family history-based genetic risk (Arm 2, n = 120), and mammography risks, genetic risk, and behavioral alternatives (Arm 3, n = 139). Willingness to delay screening or reduce screening frequency was assessed post-experiment by a set of 5-point Likert scale items. RESULTS: Women in Arm 3 reported significantly greater willingness to delay screening mammography until age 50 (mean = 0.23, SD = 1.26) compared with those in Arm 1 (mean = -0.17, SD = 1.20; p = .04). There were no significant arm differences in willingness to reduce screening frequency. Exposure to the communication messages significantly shifted women's breast cancer-related risk perceptions without increasing unwarranted cancer worry across all three arms. CONCLUSIONS: Providing women with screening information and options may help initiate challenging discussions with providers about potentially low-value screening.

The US Preventive Services Task Force does not recommend routine annual mammography screening for women aged 40­49 at average risk. This study aimed to assess the impact of theory-based persuasive messages on women's willingness to delay mammography screening until age 50 or opt for biennial screenings. In a randomized online experiment, 383 U.S. women aged 40­49 at average risk for breast cancer were assigned to three different message groups. The results showed that women exposed to messaging that included mammography risks, family history-based genetic risk, and behavioral alternatives were significantly more willing to delay screening until age 50. However, there were no significant differences in willingness to reduce screening frequency. The tested communication messages effectively shifted women's breast cancer-related risk perceptions without causing unnecessary worry. Providing women with screening information and options may help initiate challenging discussions with providers about potentially low-value screening.

Advancing precision public health for obesity in children.

Worldwide, far too many children and adolescents are living with the disease of obesity. Despite decades of public health initiatives, rates are still rising in many countries. This raises the question of whether precision public health may be a more successful approach to preventing obesity in youth. In this review, the objective was to review the literature on precision public health in the context of childhood obesity prevention and to discuss how precision public health may advance the field of childhood obesity prevention. As precision public health is a concept that is still evolving and not fully identifiable in the literature, a lack of published studies precluded a formal review. Therefore, the approach of using a broad interpretation of precision public health was used and recent advances in childhood obesity research in the areas of surveillance and risk factor identification as well as intervention, evaluation and implementation using selected studies were summarized. Encouragingly, big data from a multitude of designed and organic sources are being used in new and innovative ways to provide more granular surveillance and risk factor identification in obesity in children. Challenges were identified in terms of data access, completeness, and integration, ensuring inclusion of all members of society, ethics, and translation to policy. As precision public health advances, it may yield novel insights that can contribute to strong policies acting in concert that ultimately lead to the prevention of obesity in children.

Global distribution of functionally important CYP2C9 alleles and their inferred metabolic consequences.

BACKGROUND: Genetic variability in the cytochrome P450 CYP2C9 constitutes an important predictor for efficacy and safety of various commonly prescribed drugs, including coumarin anticoagulants, phenytoin and multiple non-steroidal anti-inflammatory drugs (NSAIDs). A global map of CYP2C9 variability and its inferred functional consequences has been lacking. RESULTS: Frequencies of eight functionally relevant CYP2C9 alleles (*2, *3, *5, *6, *8, *11, *13 and *14) were analyzed. In total, 108 original articles were identified that included genotype data from a total of 81,662 unrelated individuals across 70 countries and 40 unique ethnic groups. The results revealed that CYP2C9*2 was most abundant in Europe and the Middle East, whereas CYP2C9*3 was the main reason for reduced CYP2C9 activity across South Asia. Our data show extensive variation within superpopulations with up to tenfold differences between geographically adjacent populations in Malaysia, Thailand and Vietnam. Translation of genetic CYP2C9 variability into functional consequences indicates that up to 40% of patients in Southern Europe and the Middle East might benefit from warfarin and phenytoin dose reductions, while 3% of patients in Southern Europe and Israel are recommended to reduce starting doses of NSAIDs. CONCLUSIONS: This study provides a comprehensive map of the genetic and functional variability of CYP2C9 with high ethnogeographic resolution. The presented data can serve as a useful resource for CYP2C9 allele and phenotype frequencies and might guide the optimization of genotyping strategies, particularly for indigenous and founder populations with distinct genetic profiles.

Re-envisioning community genetics: community empowerment in preventive genomics.

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. While the integration of PGS into public health and clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of many healthcare systems to support person-centered and value-based care. This paper argues that any conversation about whether and how to design and implement PGS clinical services requires dynamic engagement with local communities, patients, and families. These parties often face the consequences, both positive and negative, of such uncertainties and should therefore drive clinical translation. As a collaborative effort between hospital stakeholders, community partners, and researchers, this paper describes a community-empowered co-design process for addressing uncertainty and making programmatic decisions about the implementation of PGS into clinical services. We provide a framework for others interested in designing clinical programs that are responsive to, and inclusive and respectful of, local communities.

Precision public-health intervention for care coordination: a real-world study.

BACKGROUND: Health emergencies disproportionally affect vulnerable populations. Digital tools can help primary care providers find, and reach, the right patients. AIM: To evaluate whether digital interventions delivered directly to GPs' clinical software were more effective at promoting primary care appointments during the COVID-19 pandemic than interventions delivered by post. DESIGN AND SETTING: Real-world, non-randomised, interventional study involving GP practices in all Australian states. METHOD: Intervention material was developed to promote care coordination for vulnerable older veterans during the COVID-19 pandemic, and sent to GPs either digitally to the clinical practice software system or in the post. The intervention material included patient-specific information sent to GPs to support care coordination, and education material sent via post to veterans identified in the administrative claims database. To evaluate the impact of intervention delivery modalities on outcomes, the time to first appointment with the primary GP was measured; a Cox proportional hazards model was used, adjusting for differences and accounting for pre-intervention appointment numbers. RESULTS: The intervention took place in April 2020, during the first weeks of COVID-19 social distancing restrictions in Australia. GPs received digital messaging for 51 052 veterans and postal messaging for 26 859 veterans. The digital group was associated with earlier appointments (adjusted hazard ratio 1.38 [1.34 to 1.41]). CONCLUSION: Data-driven digital solutions can promote care coordination at scale during national emergencies, opening up new perspectives for precision public-health initiatives.

Health Equity and Colorectal Cancer Awareness: a Community Health Educator Initiative.

Disparities in colorectal cancer (CRC) incidence and mortality persist in rural and underserved communities. Our Community Outreach and Engagement (COE) activities are grounded in a bi-directional Community-to-Bench model in which the National Outreach Network Community Health Educator (NON CHE) Screen to Save (S2S) initiative was implemented. In this study, we assessed the impact of the NON CHE S2S in rural and underserved communities. Descriptive and comparative analyses were used to examine the role of the NON CHE S2S on CRC knowledge and CRC screening intent. Data included demographics, current CRC knowledge, awareness, and future CRC health plans. A multivariate linear regression was fit to survey scores for CRC knowledge. The NON CHE S2S engaged 441 participants with 170 surveys completed. The difference in participants' CRC knowledge before and after the NON CHE S2S intervention had an overall mean of 0.92 with a standard deviation of 2.56. At baseline, White participants had significantly higher CRC knowledge scores, correctly answering 1.94 (p = 0.007) more questions on average than Black participants. After the NON CHE S2S intervention, this difference was not statistically significant. Greater than 95% of participants agreed that the NON CHE S2S sessions impacted their intent to get screened for CRC. Equity of access to health information and the health care system can be achieved with precision public health strategies. The COE bi-directional Community-to-Bench model facilitated community connections through the NON CHE and increased awareness of CRC risk reduction, screening, treatment, and research. The NON CHE combined with S2S is a powerful tool to engage communities with the greatest health care needs and positively impact an individual's intent to "get screened" for CRC.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

Steady increase of obesity prevalence in Austria: Analysis of three representative cross-sectional national health interview surveys from 2006 to 2019.

BACKGROUND: Obesity is associated with adverse health consequences throughout life. Monitoring obesity trends is important to plan and implement public heath interventions adapted to specific target groups. We aimed to analyze the development of obesity prevalence in the Austrian population using data from the most recent representative Austrian Health Interview Surveys. METHODS: The three cross-sectional Austrian health interview surveys from 2006/2007, 2014 and 2019 were used (n = 45,707). Data correction for self-reported body mass index (BMI) was applied. Sex, age, education level, employment status, country of birth, urbanization, and family status were used as sociodemographic factors. Logistic regression models were applied. RESULTS: Prevalence of obesity increased in both sexes in the study period (men 13.7% to 20.0%, women 15.2% to 17.8%, p < 0.001). Adjusted odds ratios (95% confidence interval [CI]) for the increase in obesity prevalence was 1.47 (95% CI: 1.38-1.56). In men, obesity prevalence almost doubled from 2006/2007 to 2019 in subgroups of 15-29-year-olds (4.8% to 9.0%), unemployed (13.5% to 27.6%), men born in non-EU/non-EFTA countries (13.9% to 26.2%), and not being in a relationship (8.1% to 15.4%). In women, the largest increase was found in subgroups of 30-64-year-olds (15.8% to 18.7%), women born in non-EU/non-EFTA countries (19.9% to 22.8%) and in women living in the federal capital Vienna (16.5% to 19.9%). CONCLUSION: Obesity prevalence in the Austrian population continues to rise significantly. We identified distinct subgroups with a fast-growing obesity prevalence in recent years, emphasizing the importance of regular long-term data collection as a basis for sustainable and target group-specific action planning.