Evaluation of a Health Education Intervention for Rural Preschool and Kindergarten Children in the Southeastern United States: A Cluster Randomized Trial

This research employed a matched-pairs randomized field experiment design to evaluate a classroom-based health education intervention for pre-Kindergarten and Kindergarten children in a rural region of the southeastern United States. Schools were matched on demographic characteristics, then one school from each pair was randomly assigned to the treatment group and one to the delayed treatment group. The intervention included a field trip experience and an integrated curriculum designed to increase knowledge about nutrition, physical activity, and sleep. Staff conducted individual assessments of changes in knowledge with a random sample of children from each classroom (252 children from treatment classrooms; 251 children from delayed treatment classrooms). We used a multilevel linear regression with maximum likelihood estimation to incorporate the effects of clustering at the classroom and school level while examining the effects of the intervention on individual assessment change scores. During the intervention period, an estimated 3,196 children (treatment: 1,348 students in 68 classrooms in 10 schools; delayed treatment: 1,848 students in 86 classrooms in 10 schools) participated in the intervention. Children in the treatment group had significantly larger assessment change scores than children in the delayed treatment group. Findings suggest significant beneficial effects of the intervention on health knowledge

Making COVID-19-Related Decisions: A Qualitative Study of University Students

Background: SARS-Cov-2 (Coronavirus Disease or COVID-19) has impacted society greatly since its arrival to the United States. More specifically, college students have had to modify their behaviors on campus to minimize the spread of the virus.Purpose: The purpose of this study was to identify benefits and barriers to testing, reporting, and quarantining of undergraduate college students attending a large southeastern university.Methods: Undergraduate students were asked to complete an open-ended Qualtrics survey to share their perceived benefits and barriers to engage in behaviors to detect and prevent the spread of COVID-19. Data was analyzed via Grounded Theory techniques to determine codes and subsequent themes related to the probes.Results: Themes emerged in the categories of (a) Influencing Factors, and (b) Outcomes of the Health Behavior with 12 in the former and six in the latter. Emphasis on disclosure of COVID-19 status could be beneficial in preventing the spread of the virus on large college and university campuses.Conclusion: The data collected in this study can be used to inform COVID-19-related policies and health communication campaigns at similar colleges and universities

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Impact of a Risk-Based Breast Cancer Screening Decision Aid on Initiation of Mammography Among Younger Women: Report of a Randomized Trial

Background. Guidelines recommend that initiation of breast cancer screening (BCS) among women aged 40 to 49 years include a shared decision-making process. The objective of this study is to evaluate the effect of a breast cancer screening patient decision-aid (BCS-PtDA) on the strength of the relationship between individual risk and the decision to initiate BCS, knowledge, and decisional conflict. Methods. We conducted a randomized clinical trial of a BCS-PtDA that included individual risk estimates compared with usual care. Participants were women 39 to 48 years of age with no previous mammogram. Primary outcomes were strength of association between breast cancer risk and mammography uptake at 12 months, knowledge, and decisional conflict. Results. Of 204 participants, 65% were Black, the median age (interquartile range [IQR]) was 40.0 years (39.0–42.0), and median (IQR) breast cancer lifetime risk was 9.7% (9.2–11.1). Women who received mammography at 12 months had higher breast cancer lifetime risk than women who had not in both intervention (mean, 95% CI): 12.2% (10.8–13.6) versus 10.5% (9.8–11.2), P = 0.04, and control groups: 11.8% (10.4–13.1) versus 9.9% (9.2–10.6), P = 0.02. However, there was no difference between groups in the strength of association between mammography uptake and breast cancer risk ( P = 0.87). Follow-up knowledge (0–5) was greater in the intervention versus control group (mean, 95% CI): 3.84 (3.5–4.2) versus 3.17 (2.8–3.5), P = 0.01. There was no change in decisional conflict score (1–100) between the intervention versus control group (mean, 95% CI): 24.8 (19.5–30.2) versus 32.4 (25.9–39.0), P = 0.07. Conclusions. The BCS-PtDA improved knowledge but did not affect risk-based decision making regarding age of initiation of BCS. These findings indicate the complexity of changing behaviors to incorporate objective risk in the medical decision-making process

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs