Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

Community Activities in Primary Care: A Literature Review

Community health promotion activities are a useful tool for a proactive approach to healthy lifestyles. However, the implementation of these types of activities at health centers is not standardized. The aim of this review was to analyse the characteristics of community activities undertaken in the primary care setting and substantiate available evidence on their health impact. We conducted a bibliographic review until November 15th, 2023 in the TRIPDATABASE, MEDLINE, EMBASE, and DIALNET databases. We included original papers on interventions, community activities, and actions and/or social prescriptions which had been implemented in a Primary Care setting, included a group approach in at least one session, and described some type of evaluation of the intervention applied. Studies targeted at professionals and those without involvement of the primary care team were excluded. The search identified 1912 potential studies. We included a total of 30 studies, comprising 11 randomized clinical trials, 14 quasi-experimental studies, 1 cohort study, and 4 qualitative studies. The issues most frequently addressed in community activities were healthy habits, physical activity, cardiovascular diseases and diabetes. Community activities can improve the physical and psychological environment of their participants, as well as their level of knowledge about the issues addressed. That said, however, implementation of these types of interventions is not uniform. The existence of a professional community-activity liaison officer at health centers, who would help integrate the health system with the community sector, could serve to standardize implementation and maximize the health impact of these types of interventions.

Hepatitis A Vaccination Coverage Among People With Chronic Liver Disease in England (HEALD): Protocol for a Retrospective Cohort Study

BACKGROUND

Hepatitis A outbreaks in the United Kingdom are uncommon. Most people develop mild to moderate symptoms that resolve, without sequelae, within months. However, in high-risk groups, including those with underlying chronic liver disease (CLD), hepatitis A infection can be severe, with a higher risk of mortality and morbidity. The Health Security Agency and the National Institute of Health and Care Excellence recommend preexposure hepatitis A vaccination given in 2 doses to people with CLD, regardless of its cause. There are currently no published reports of vaccination coverage for people with CLD in England or internationally.

OBJECTIVE

This study aims to describe hepatitis A vaccination coverage in adults with CLD in a UK primary care setting and compare liver disease etiology, sociodemographic characteristics, and comorbidities in people who are and are not exposed to the hepatitis A vaccine.

METHODS

We will conduct a retrospective cohort study with data from the Primary Care Sentinel Cohort of the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub database, which is nationally representative of the English population. We will include people aged 18 years and older who have been registered in general practices in the Research and Surveillance Centre network and have a record of CLD between January 1, 2012, and December 31, 2022, including those with alcohol-related liver disease, chronic hepatitis B, chronic hepatitis C, nonalcohol fatty liver disease, Wilson disease, hemochromatosis, and autoimmune hepatitis. We will carefully curate variables using the Systematized Nomenclature of Medicine Clinical Terms. We will report the sociodemographic characteristics of those who are vaccinated. These include age, gender, ethnicity, population density, region, socioeconomic status (measured using the index of multiple deprivation), obesity, alcohol consumption, and smoking. Hepatitis A vaccination coverage for 1 and 2 doses will be calculated using an estimate of the CLD population as the denominator. We will analyze the baseline characteristics using descriptive statistics, including measures of dispersion. Pairwise comparisons of case-mix characteristics, comorbidities, and complications will be reported according to vaccination status. A multistate survival model will be fitted to estimate the transition probabilities among four states: (1) diagnosed with CLD, (2) first dose of hepatitis A vaccination, (3) second dose of hepatitis A vaccination, and (4) death. This will identify any potential disparities in how people with CLD get vaccinated.

RESULTS

The Research and Surveillance Centre population comprises over 8 million people. The reported incidence of CLD is 20.7 cases per 100,000. International estimates of hepatitis A vaccine coverage vary between 10% and 50% in this group.

CONCLUSIONS

This study will describe the uptake of the hepatitis A vaccine in people with CLD and report any disparities or differences in the characteristics of the vaccinated population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/51861.

Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO)

OBJECTIVE

Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way.

MATERIAL AND METHODS

Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed.

RESULTS

We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results.

DISCUSSION

SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations.

CONCLUSION

SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Social Prescription Interventions Addressing Social Isolation and Loneliness in Older Adults: Meta-Review Integrating On-the-Ground Resources

BACKGROUND

Social prescription programs represent a viable solution to linking primary care patients to nonmedical community resources for improving patient well-being. However, their success depends on the integration of patient needs with local resources. This integration could be accelerated by digital tools that use expressive ontology to organize knowledge resources, thus enabling the seamless navigation of diverse community interventions and services tailored to the needs of individual users. This infrastructure bears particular relevance for older adults, who experience a range of social needs that impact their health, including social isolation and loneliness. An essential first step in enabling knowledge mobilization and the successful implementation of social prescription initiatives to meet the social needs of older adults is to incorporate the evidence-based academic literature on what works, with on-the-ground solutions in the community.

OBJECTIVE

This study aims to integrate scientific evidence with on-the-ground knowledge to build a comprehensive list of intervention terms and keywords related to reducing social isolation and loneliness in older adults.

METHODS

A meta-review was conducted using a search strategy combining terms related to older adult population, social isolation and loneliness, and study types relevant to reviews using 5 databases. Review extraction included intervention characteristics, outcomes (social [eg, loneliness, social isolation, and social support] or mental health [eg, psychological well-being, depression, and anxiety]), and effectiveness (reported as consistent, mixed, or not supported). Terms related to identified intervention types were extracted from the reviewed literature as well as descriptions of corresponding community services in Montréal, Canada, available from web-based regional, municipal, and community data sources.

RESULTS

The meta-review identified 11 intervention types addressing social isolation and loneliness in older adults by either increasing social interactions, providing instrumental support, promoting mental and physical well-being, or providing home and community care. Group-based social activities, support groups with educational elements, recreational activities, and training or use of information and communication technologies were the most effective in improving outcomes. Examples of most intervention types were found in community data sources. Terms derived from the literature that were the most commonly congruent with those describing existing community services were related to telehealth, recreational activities, and psychological therapy. However, several discrepancies were observed between review-based terms and those addressing the available services.

CONCLUSIONS

A range of interventions found to be effective at addressing social isolation and loneliness or their impact on mental health were identified from the literature, and many of these interventions were represented in services available to older residents in Montréal, Canada. However, different terms were occasionally used to describe or categorize similar services across data sources. Establishing an efficient means of identifying and structuring such sources is important to facilitate referrals and help-seeking behaviors of older adults and for strategic planning of resources.

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.

Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.

Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.