Hansen's Disease and Complications among Marshallese Persons Residing in Northwest Arkansas, 2003-2017

Moana: Alternate surveillance for COVID-19 in a Unique Population (MASC-UP)

Objective

Create a longitudinal, multi-modal and multi-level surveillance cohort that targets early detection of symptomatic and asymptomatic COVID-19 cases among Native Hawaiian and Pacific Islander adults in the Continental US and identify effective modalities for participatory disease surveillance and sustainably integrate them into ongoing COVID-19 and other public health surveillance efforts.

Materials and methods

We recruited cohorts from three sites: Federal Way, WA; Springdale, AR; and remotely. Participants received a survey that included demographic characteristics and questions regarding COVID-19. Participants completed symptom checks via text message every month and recorded their temperature daily using a Kinsa smart thermometer.

Results

Recruitment and data collection is ongoing. Presently, 441 adults have consented to participate. One-third of participants were classified as essential workers during the pandemic.

Discussion

Over the past 18 months, we have improved our strategies to elicit better data from participants and have learned from some of the weaknesses in our initial deployment of this type of surveillance system. Other limitations stem from historic inequities and barriers which limited Native Hawaiian and Pacific Island representation in academic and clinical environments. One manifestation of this was the limited ability to provide study materials and support in multiple languages. We hope that continued partnership with the community will allow further opportunities to help restore trust in academic and medical institutions, thus generating knowledge to advance health equity.

Conclusion

This participatory disease surveillance mechanism complements traditional surveillance systems by engaging underserved communities. We may also gain insights generalizable to other pathogens of concern.

"I've Never Been to a Doctor": Health Care Access for the Marshallese in Washington State

The Ri Majel (Marshallese) migrants of Washington State have endured health inequities and unique laws dictating their access to health care once they arrive to the United States. These health inequities can be seen to be a result of historical trauma and militarization of their islands. The research question was an inquiry regarding access to health care for the Ri Majel in Washington State. We first provide detailed historical data in the background to contextualize our research inquiry. We interviewed 12 people and using manifest content analysis found 2 main themes regarding the health of the Ri Majel: (1) health care access and inequity and (2) historical trauma and embodiment. Health care access was impeded by (1) ongoing effects of radiation, (2) repeated denial of services, (3) lack of health care and insurance, (4) lack of language interpretation during health care visits, and (5) poverty. Historical trauma and embodiment were evidenced by these findings: (1) illness and early mortality; (2) provider lack of knowledge and understanding of the Ri Majel; (3) structural discrimination; (4) feelings of sadness and despair; (5) shyness and humility; and (6) a sense of "cannot/will not" and fatalism. Our findings demonstrate the need to examine structural factors when assessing health inequities and a need to understand and mitigate the effects of historical trauma enacted by structural racism, violence, and colonialism. Strategies to mitigate the embodiment of historical trauma require further investigation.

Precision information extraction for rare disease epidemiology at scale

BACKGROUND

The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations.

METHODS

In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies.

RESULTS

We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet's collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm.

CONCLUSIONS

EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.