Online journal of public health informatics最新文献

Background: For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days' supply (DS) for each prescription. Unfortunately, often the DS or the information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substitute incorrect values.

Objective: This study aims to apply a variety of methods for managing incomplete and missing data to enhance the accuracy of calculating DS for all medications and drug forms alike. Furthermore, to describe the effect of applied methods on the medication adherence calculated on real-world data.

Methods: A dataset comprising prescription records from a 10% (150,824 patients) random sample of the Estonian population between 2012 and 2019 was used. The workflow consisted of 3 steps: data cleaning, imputation, and calculation of DS. For imputation, different methods were combined, such as calculating mode-based daily dose, or using usage guidelines from the Summary of Product Characteristics or legislation. DS was calculated based on the provided daily dose or imputed value. To evaluate the impact of data cleaning, medication adherence for the baseline dataset and corrected dataset for 2 time periods, 2012-2015 and 2017-2019, was calculated and compared.

Results: The drug forms with the lowest proportion of correct DS provided were insulin injections (2601/82,867, 3.1%) and intravaginal contraceptives (1692/21,145, 8%) while the highest proportion of DS was provided for inhalation medication (78,541/126,588, 62%), oral drops (52,085/98,221, 53%) and tablets, capsules, suppositories (2,828,617/6,176,585, 45.8%). As a result of applying different imputation approaches, we successfully found the DS for 98.3% (7,415,347/7,544,892) of dispensed prescriptions. For the remaining 1.7% (129,545/7,544,892) of prescriptions, DS could not be imputed nor calculated with these methods. As for the medication adherence, the distinction between 2 observed time periods was more distinct in the baseline dataset compared with the corrected dataset for most of the drug groups, indicating that the applied correction methods had lessened the stark contrast.

Conclusions: In summary, our study demonstrated that with a carefully designed imputation pipeline where data-driven imputation is combined with domain knowledge and literature information, it is possible to meaningfully improve the quality of prescription datasets and generate more accurate and consistent adherence metrics across various drug forms. Nonetheless, future efforts should continue to refine imputation techniques, incorporate machine learning approaches where appropriate, and expand validation efforts using external benchmarks or clinical outcomes.

Background: Public health data integration and automation systems are crucial for effective health care delivery and public health surveillance. However, the factors associated with hospitals' adoption and successful implementation remain inadequately explored.

Objective: This study aims to examine how hospital characteristics influence the adoption of public health data integration and automation.

Methods: We analyzed 2277 hospitals from the 2023 American Hospital Association Annual Survey and its Health Information Technology supplement, focusing on 6 public health reporting categories. Multivariable logistic regression models were used to examine the association between hospital characteristics and the 2 primary outcomes: active electronic data submission and use of automated transmission processes.

Results: System-affiliated and not-for-profit hospitals demonstrated significantly higher rates of electronic data submission and automated reporting across most categories (odds ratio [OR] 1.70-2.27; P<.001). Rural hospitals showed lower adoption rates in immunization registry (OR 0.77, 95% CI 0.61-0.97), public health registry (OR 0.67, 95% CI 0.46-0.97), and clinical data registry reporting (OR 0.77, 95% CI 0.60-0.98). Larger hospitals were more likely to implement electronic reporting, with medium and large hospitals showing stronger engagement in syndromic surveillance reporting (OR 1.52, 95% CI 1.06-2.19 and OR 2.29, 95% CI 1.17-4.46, respectively). Teaching status was significantly associated only with clinical data registry reporting (OR 2.66, 95% CI 1.56-4.52 for major teaching hospitals).

Conclusions: Hospital characteristics, particularly system affiliation, ownership type, and geographic location, are strongly associated with public health data integration and automation capabilities. Findings suggest targeted interventions are needed to address disparities in smaller and rural facilities to ensure equitable advancement of public health reporting infrastructure.

Background: COVID-19 forecasting models have been used to inform decision-making around resource allocation and intervention decisions, such as hospital beds or stay-at-home orders. State-of-the-art forecasting models often use multimodal data, including mobility or sociodemographic data, to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which affects the fairness of COVID-19 predictions across racial and ethnic groups.

Objective: This study aims to introduce a fairness correction method that works for forecasting COVID-19 cases at an aggregate geographic level.

Methods: We use hard and soft error parity analyses on existing fairness frameworks and demonstrate that our proposed method, Demographic Optimization (DemOpts), performs better in both scenarios.

Results: We first demonstrate that state-of-the-art COVID-19 deep learning models produce mean prediction errors that are significantly different across racial and ethnic groups at larger geographic scales. We then propose a novel debiasing method, DemOpts, to increase the fairness of deep learning-based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity than other state-of-the-art debiasing approaches, thus effectively reducing the differences in the mean error distributions across racial and ethnic groups.

Conclusions: We introduce DemOpts, which reduces error parity differences compared with other approaches and generates fairer forecasting models compared with other approaches in the literature.

Background: Mini-implants, or temporary anchorage devices (TADs), have transformed modern orthodontic practice by offering stable, minimally invasive anchorage for complex tooth movements. Despite their proven effectiveness, their use varies widely across regions, often influenced by clinicians' knowledge, confidence, and training.

Objective: This study explored the knowledge, attitudes, and practices of orthodontic professionals toward mini-implant use in the Kurdistan Region-Iraq and examined how experience and professional background shape their adoption in daily clinical work.

Methods: A cross-sectional online survey was conducted between April and September 2025 among orthodontic faculty and postgraduate trainees from seven dental colleges in the Kurdistan Region. The validated questionnaire assessed participants' demographic details and three key domains-knowledge, perception, and attitude-using Likert-scale responses. Data were analyzed with SPSS version 28.0 using Mann-Whitney U, Kruskal-Wallis, and Spearman's correlation tests, with significance set at P ≤ .05.

Results: A total of 175 orthodontic practitioners completed the survey (57% postgraduate trainees, 43% faculty members). Postgraduates demonstrated significantly higher knowledge scores (3.66 ± 0.49 vs 3.16 ± 0.48; P = .01) and perception scores (3.29 ± 0.60 vs 2.39 ± 0.58; P = .02). Immediate loading was preferred by 80% of postgraduates compared with 40% of faculty (P = .001), while radiographic guidance was selected as the safest placement method by 75% of postgraduates versus 40% of faculty (P < .001). Younger clinicians (<35 years) and those with less than five years of experience showed significantly higher perception scores (P = .01). Knowledge, perception, and attitude were strongly correlated (r = .74; P < .001), indicating that increased understanding promotes more positive attitudes toward miniscrew use.

Conclusions: Orthodontists in the Kurdistan Region generally hold favorable views toward mini-implants, yet differences in confidence and hands-on experience remain evident across generations.

Clinicaltrial:

Background: Loneliness is a dynamic phenomenon that can be investigated using social media and web data.

Objective: This study aims to introduce a framework for studying loneliness through social media and online data sources. A case study is presented to demonstrate the deployment of this framework and its effectiveness in collecting and analyzing data related to loneliness.

Methods: Our proposed framework involves collecting data from various social media and online sources. We discuss the modalities of analyzing the collected data based on the framework's defined purpose. The analysis was conducted using tools such as Google Trends, the News application programming interface, X (formerly known as Twitter), Reddit, and other social media platforms. Different types of data were categorized according to the proposed framework to understand and study loneliness comprehensively.

Results: The results demonstrate the effectiveness of our proposed framework in collecting various types of data related to loneliness. Tools such as Google Trends and the News application programming interface provided insights into loneliness trends in specific regions. Social media platforms offered behavioral data on loneliness, which were analyzed using sentiment analysis and social intelligence techniques. Correlations between loneliness and personal-emotional and socioeconomic categories were identified through this analysis.

Conclusions: The framework and tools discussed in this paper complement psychosocial approaches to loneliness, which typically rely on self-report measurements. By incorporating online data perspectives, our framework provides valuable insights into loneliness dynamics, enhancing our understanding of this complex phenomenon.

Background: Considering the rapid digital transformation, older adults are increasingly relying on online health information-seeking (OHIS) to support healthy aging. However, disparities in their digital competence levels (the ability to effectively use digital tools) and health literacy (the ability to access, understand, appraise, and apply health information) may influence engagement in OHIS.

Objective: This paper examines the prevalence of OHIS among older adults in Switzerland and identifies their motivations, barriers, and predictors of use. The objective is to determine key factors that promote or hinder OHIS use among older internet users.

Methods: A cross-sectional survey was conducted with 1261 internet users aged 60 years and older living in Switzerland (mean age 70.1, SD 7.3 years; 539/1261, 42.7% female). Descriptive analyses and hierarchical binary logistic regression models were used.

Results: Overall, 77.6% (969/1248) of participants engaged in OHIS in their everyday lives. Subjective health status, internet use frequency, trust in online health information (OHI), and digital competence level significantly influenced OHIS use. Participants reporting good to very good health were less likely to engage in OHIS compared to those in poorer health (odds ratio [OR] 0.496, 95% CI 0.307-0.801; P=.004). Higher likelihood of OHIS use was associated with (almost) daily versus less frequent internet use (OR 1.550, 95% CI 1.011-2.376; P=.04), general trust versus distrust in OHI (OR 5.784, 95% CI 4.044-8.272; P<.001), and advanced versus low digital competence (OR 3.108, 95% CI 1.385-6.975; P=.006); health literacy was not a significant predictor of OHIS use (OR 0.912, 95% CI 0.393-2.117; P=.83, excellent vs deficient [reference]). Among OHIS users (n=969), the most common frequently indicated motivation for use (672/969, 69.3%) was to gain a better understanding of health conditions. Among nonusers (n=279), the most frequently indicated barriers were difficulties in assessing the credibility of information (159/279, 57%), distrust in the effectiveness of information provided (129/279, 46.2%), and concerns about dubious providers or spam (93/279, 33.3%).

Conclusions: Digital competence, frequent internet use, and trust in OHI are critical for OHIS engagement among older adults. Programs to strengthen digital competencies in later life and initiatives to enhance the credibility of online health resources are essential to reduce digital disparities and support healthy aging. Notably, health literacy did not emerge as a significant factor in OHIS use, but digital competence did, suggesting that digital competence is most critical to OHIS use.

Unlabelled: Digital-based health interventions (DHIs), defined as health services delivered electronically, have demonstrated effectiveness in promoting health outcomes. However, DHIs often suffer from low user retention, a challenge attributed to limited attention to sociocultural determinants and insufficient user engagement strategies. This paper explores participatory animation (PA), a collaborative methodology that engages community partners in co-creating animated content, as a strategy to improve DHI retention and effectiveness. Drawing from existing literature, this viewpoint examines the theoretical foundations and practical affordances of PA for enhancing DHIs. We describe PA as a multistep production process that integrates participant-driven oral and visual design contributions into multimedia outputs for use in DHIs. Here, PA shows promise in producing engaging and culturally resonant content, with the potential to improve intervention uptake and sustain user engagement. Despite these affordances, PA remains underused in health research. Given the growing urgency to develop effective, equitable DHIs, PA offers a novel, community-informed approach for enhancing both design and implementation. This paper positions PA as a methodological frontier for DHI science.

Background: The COVID-19 pandemic has boosted telehealth adoption among clinical nutritionists globally. However, there is a research gap in Saudi Arabia concerning telehealth's prevalence and effectiveness in dietetics practice.

Objective: This study aims to evaluate telehealth implementation during the pandemic in Saudi Arabia.

Methods: In this cross-sectional study, a web-based survey was used and distributed in several Saudi Arabian regions between December 2022 and May 2023. A convenience sample of 306 clinical registered dietitian nutritionists (RDNs) in public and private health care facilities who met the study's inclusion criteria was included in this study.

Results: During the COVID-19 pandemic, 56% (172/306) of RDNs used telehealth, showing significant differences in sociodemographics and telehealth knowledge at health care facilities (P=.04). Notable gender disparities were observed in years of experience, with 78% (61/78) of male dietitians working in public hospitals (P=.001 and P<.004). The main telehealth nutrition services provided included nutrition education (21%, 64/306), nutrition counseling (19%, 58/306), and nutrition monitoring (17%, 52/306). Telenutrition purposes primarily focused on nutrition education (21%), supporting weight and diet management (17.15%, 15%), and the management of chronic disease (14%, 43,306). Additionally, a smaller percentage of RDNs (8%-9%) used telehealth for the nutrition care process and health assessment, while no respondents reported using telehealth for sport nutrition services. Overall, 90% (275/306) of RDNs reported that they routinely incorporated telehealth into their practice. Common obstacles reported by RDNs using telehealth were internet connectivity issues (46%), difficulties in coordinating with patients (22%), and patient disengagement with a lack of motivation (13%).

Conclusions: Our findings underscore the increasing adoption of telehealth by RDNs during the pandemic, highlighting its crucial role in nutrition services. The study suggests that technology enhancements and training initiatives can improve telehealth effectiveness, highlighting the need for further research in this dynamic field.

College years represent a pivotal phase as students transition into adulthood, a period marked by heightened vulnerability to mental health challenges. Beyond the high prevalence of common mental health issues, a large treatment gap (driven by both supply-side and demand-side factors) exacerbates the overall burden. Furthermore, students in higher education frequently experience psychological distress and subthreshold symptoms that impair well-being and daily functioning. Globally, technology-based mental health solutions have emerged as an important strategy to address unmet needs, with a growing evidence base across populations. Research has increasingly focused on examining digital mental health interventions for college students. Against this backdrop, we examine the challenges within India's large higher education system-which serves approximately 43 million students-and the expanding role of technology in this sector. We explore the potential for leveraging technology-based solutions to enhance student mental health initiatives within higher education institutions, considering relevant policies and guidelines that provide an impetus to these efforts. We reflect upon challenges and opportunities for implementing digital mental health interventions in Indian higher education, and propose strategic actions at institutional and governmental levels. Key considerations include data governance, safety, transparency, positioning of digital initiatives relative to in-person care, safeguards for content quality, provision of interventions at varying intensities, and recommendations for policy, governmental support, and research to optimize the use of technology for student mental health in institutes of higher education in India.