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Purpose: Refugee and immigrant patients face significant barriers to health care and are more likely to have poorly controlled chronic disease than the general U.S. population. I-Care aims to improve health equity for refugees and immigrants who face a disproportionate burden of chronic disease. Methods: Refugees and immigrants with uncontrolled diabetes and associated cardiovascular risk factors were enrolled in a care management program within an academic adult medicine clinic. The program utilized a care manager to coordinate care and services between designated primary care providers, affiliated clinical teams, and community partners. Health literacy, chronic disease parameters, and care utilization were assessed at enrollment and 8-12 months later. Results: A total of 50 refugees and immigrants were followed for 8 to 12 months. Clinical parameters found a reduced mean HbA1c from 9.32 to 8.60 (p=0.05) and reduced low-density lipoprotein mean from 96.22 to 86.60 (p=0.01). The frequency of normal blood pressures was 9 (18%) at enrollment and 16 (32%) at 1 year. The cumulative frequency of emergency room visits decreased from 66% to 36% and hospitalizations from 22% to 8%. Rates of comprehensive care monitoring, including monofilament testing and one-time ophthalmology visits, increased from 60% to 82% and from 32% to 42%, respectively. Cumulative frequency of interdisciplinary support engagement with pharmacy and nutrition visits increased from 58% to 78% and from 26% to 38%, respectively. Conclusion: This program highlights the importance of a multidisciplinary community-engaged care model that has demonstrated improvement in quality metrics and health care costs for refugees and immigrants.
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BACKGROUND: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. METHODOLOGY/PRINCIPAL FINDINGS: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. CONCLUSIONS/SIGNIFICANCE: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.