Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Term Coverage.

As part of the Open Government Initiative, the United States federal government published datasets to increase collaboration, transparency, consumer participation, and research, and are available online at HealthData.gov. Currently, HealthData.gov does not adequately support the accessibility goal of the Open Government Initiative due to issues of retrieving relevant data because of inadequately cataloguing and lack of indexing with a standardized terminology. Given the commonalities between the HealthData.gov and MEDLINE metadata, Medical Subject Headings (MeSH) may offer an indexing solution, but there needs to be a formal evaluation of the efficacy of MeSH for covering the dataset concepts. The purpose of this study was to determine if MeSH adequately covers the HealthData.gov concepts. The noun and noun phrases from the HealthData.gov metadata were extracted and mapped to MeSH using MetaMap. The frequency of no exact, partical and no matches with MeSH terms were determined. The results of this study revealed that about 70% of the HealthData.gov concepts partially or exactly matched MeSH terms. Therefore, MeSH may be a favorable terminology for indexing the HealthData.gov datasets.

Traumatic brain injury rehabilitation: analysis of common data elements.

Physical medicine rehabilitation interventions for post-acute traumatic brain injury (TBI) are heterogeneous and subject to differences based on multi-disciplinary treatment plans [1]. There is no universal knowledge representation (KR) model for TBI rehabilitation which impedes data collection, aggregation, computation, and sharing. This paper describes results of an analysis of the National Institute for Neurological Disorders and Stroke (NINDS) TBI "Common Data Elements" (CDE) clinical data standardization set. We conducted this to understand current TBI rehabilitation KR and as a foundational step toward the creation of a domain ontology. A content coverage study was performed on the "Treatment/Intervention" sub-set of CDEs. Results show that coverage of the CDEs is broad but lacks depth to represent the context of data collection in the TBI rehabilitation process. Next steps will be development of a KR model and identification and validation of domain concepts for a foundational ontology.

Patterns of tooth loss in older adults with and without dementia: a retrospective study based on a Minnesota cohort.

OBJECTIVES: To study tooth loss patterns in older adults with dementia. DESIGN: Retrospective longitudinal study. SETTING: A community-based geriatric dental clinic in Minnesota. PARTICIPANTS: Four hundred ninety-one older adults who presented to the study clinic as new patients during the study period, remained dentate after finishing the initial treatment plan, and returned for care at least once thereafter were retrospectively selected. One hundred nineteen elderly people with International Classification of Diseases, Ninth Revision, codes 290.x, 294.1, or 331.2 or a plain-text diagnosis of dementia, Alzheimer's disease, or chronic brain syndrome in the medical history were considered having dementia. INTERVENTION: All existing dental conditions were treated before enrollment. Dental treatment was continually provided for all participants during follow-up. MEASUREMENTS: Tooth loss patterns, including time to first tooth loss, number of tooth loss events, and number of teeth lost per patient-year were estimated and compared for participants with and without dementia using Cox, Poisson, and negative-binomial regressions. RESULTS: Participants with dementia arrived with an average of 18 and those without dementia with an average of 20 teeth; 27% of remaining teeth in the group with dementia were decayed or retained roots, higher than in the group without dementia (P<.001). Patterns of tooth loss did not significantly differ between the two groups; 11% of participants in both groups had lost teeth by 12 months of follow-up. By 48 months, 31% of participants without dementia and 37% of participants with dementia had lost at least one tooth (P=.50). On average, 15% of participants in both groups lost at least one tooth each year. Mean numbers of teeth lost in 5 years were 1.21 for participants with dementia and 1.01 for participants without dementia (P=.89). CONCLUSION: Based on data available in a community-based geriatric dental clinic, dementia was not associated with tooth loss. Although their oral health was poor at arrival, participants with dementia maintained their dentition as well as participants without dementia when dental treatment was provided.

Predicting tooth loss for older adults with special needs.

OBJECTIVES: Older Adults with Special Needs (OASN) have more oral health needs compared with healthy, independent elders. Currently, little is known about tooth loss, a key indicator of oral function loss, among OASN. Risk assessment is primarily based on clinical experience rather than scientific evidence, raising concerns for quality of care. The study's objective was to develop an evidence-based model to quantitatively predict tooth loss for OASN. METHODS: Four hundred ninety-one dentate older adults, including 235 from long-term care facilities, were retrospectively recruited. Subjects were treated and brought to a state of oral health before enrollment. Medical and dental assessments were abstracted from dental records and used to predict risk of tooth loss. Tooth loss events were recorded for subjects during follow-up. Multivariate negative-binomial regression was used, starting with 27 risk factors and removing variables using Akaike's Information Criterion. Pearson's correlation was then conducted to evaluate the overall fit of the final fitted model. RESULTS: The final fitted model included eight predictors. Among them, age, number of decayed/broken teeth at arrival, anticholinergic burden of medications and physical mobility were associated with risk of tooth loss in OASN (P ≤ 0.05). Internal validation indicated satisfactory fit of the final fitted model. CONCLUSION: An evidence-based model with eight predictors was developed to quantitatively predict risk of tooth loss for OASN at the individual level.