OBJECTIVE: Granulomatous mastitis (GM) is a benign breast disease that can have an extended clinical course impacting quality of life and causing breast disfigurement. Granulomatous mastitis has been studied throughout the world; however, less is known about GM patients in the United States. We aim to identify demographic and socioeconomic factors associated with GM in the United States.
METHODS: An IRB-approved retrospective case-control study was performed of 92 patients with biopsy-proven GM at two institutions in Los Angeles, California: a safety-net hospital and an academic institution. Age-matched controls were selected from patients presenting for diagnostic breast imaging. Demographic and socioeconomic characteristics were collected. Data were analyzed using univariable test for odds ratios (ORs) with 95% confidence intervals (CIs) and multivariable conditional logistic regression.
RESULTS: Patients with GM were more likely to prefer Spanish language (OR 6.20, 95% CI: 2.71%-14.18%), identify as Hispanic/Latina (OR 5.18, 95% CI: 2.38%-11.30%), and be born in Mexico (OR 3.85, 95% CI: 1.23%-12.02%). Cases were more likely to have no primary care provider (OR 3.76, 95% CI: 1.97%-7.14%) and use California Medicaid for undocumented adults (OR 3.65, 95% CI: 1.89%-7.08%). In the multivariable analysis, participants who preferred Spanish language had four times higher odds of GM versus those who preferred English language (OR 4.32, 95% CI: 1.38%-13.54%).
CONCLUSIONS: Patients with GM may have barriers to health care access, such as preferring Spanish language, being an undocumented immigrant, and not having a primary care provider. Given these health care disparities, further research is needed to identify risk factors, etiologies, and treatments for this subset of GM patients.

Background We evaluate nationwide trends and urban-rural disparities in case fatality (in-hospital mortality) and discharge dispositions among patients with primary intracerebral hemorrhage (ICH). Methods and Results In this repeated cross-sectional study, we identified adult patients (≥18 years of age) with primary ICH from the National Inpatient Sample (2004-2018). Using a series of survey design Poisson regression models, with hospital location-time interaction, we report the adjusted risk ratio (aRR), 95% CI, and average marginal effect (AME) for factors associated with ICH case fatality and discharge dispositions. We performed a stratified analysis of each model among patients with extreme loss of function and minor to major loss of function. We identified 908 557 primary ICH hospitalizations (overall mean age [SD], 69.0 [15.0] years; 445 301 [49.0%] women; 49 884 [5.5%] rural ICH hospitalizations). The crude ICH case fatality rate was 25.3% (urban hospitals: 24.9%, rural hospitals:32.5%). Urban (versus rural) hospital patients had a lower likelihood of ICH case fatality (aRR, 0.86 [95% CI, 0.83-0.89]). ICH case fatality is declining over time; however, it is declining faster in urban hospitals (AME, -0.049 [95% CI, -0.051 to -0.047]) compared with rural hospitals (AME, -0.034 [95% CI, -0.040 to -0.027]). Conversely, home discharge is increasing significantly among urban hospitals (AME, 0.011 [95% CI, 0.008-0.014]) but not significantly changing in rural hospitals (AME, -0.001 [95% CI, -0.010 to 0.007]). Among patients with extreme loss of function, hospital location was not significantly associated with ICH case fatality or home discharge. Conclusions Improving access to neurocritical care resources, particularly in resource-limited communities, may reduce the ICH outcomes disparity gap.

BACKGROUND: Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes for marginalized populations across a range of clinical contexts by addressing barriers to health care, including social determinants of health (SDoHs). However, it can be challenging for navigators to identify SDoHs by asking patients directly because of many factors, including patients\' reluctance to disclose information, communication barriers, and the variable resources and experience levels of patient navigators. Navigators could benefit from strategies that augment their ability to gather SDoH data. Machine learning can be leveraged as one of these strategies to identify SDoH-related barriers. This could further improve health outcomes, particularly in underserved populations.
OBJECTIVE: In this formative study, we explored novel machine learning-based approaches to predict SDoHs in 2 Chicago area PN studies. In the first approach, we applied machine learning to data that include comments and interaction details between patients and navigators, whereas the second approach augmented patients\' demographic information. This paper presents the results of these experiments and provides recommendations for data collection and the application of machine learning techniques more generally to the problem of predicting SDoHs.
METHODS: We conducted 2 experiments to explore the feasibility of using machine learning to predict patients\' SDoHs using data collected from PN research. The machine learning algorithms were trained on data collected from 2 Chicago area PN studies. In the first experiment, we compared several machine learning algorithms (logistic regression, random forest, support vector machine, artificial neural network, and Gaussian naive Bayes) to predict SDoHs from both patient demographics and navigator\'s encounter data over time. In the second experiment, we used multiclass classification with augmented information, such as transportation time to a hospital, to predict multiple SDoHs for each patient.
RESULTS: In the first experiment, the random forest classifier achieved the highest accuracy among the classifiers tested. The overall accuracy to predict SDoHs was 71.3%. In the second experiment, multiclass classification effectively predicted a few patients\' SDoHs based purely on demographic and augmented data. The best accuracy of these predictions overall was 73%. However, both experiments yielded high variability in individual SDoH predictions and correlations that become salient among SDoHs.
CONCLUSIONS: To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients\' SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.

Eating disorders typically have a protracted course, marked by significant morbidity. Male adolescents and adolescents of color are at risk of delayed care. Primary care providers are well-positioned to identify eating disorders early and initiate treatment. This case report describes an adaptation of Family-Based Treatment delivered by a primary care provider to an Asian-American male adolescent from an immigrant family with restrictive anorexia nervosa. The adolescent achieved full-weight restoration and remission of his anorexia through treatment in primary care. Embedding eating disorder treatment within primary care could improve detection, engagement, and retention in treatment among young people from diverse backgrounds.

Global disparities in breast cancer care become particularly evident when patients seek definitive care in the United States (USA) after receiving a breast cancer diagnosis and initiating care in low- and middle-income countries (LMICs). We performed a retrospective review of 26 patients with breast cancer who immigrated from LMICs and received care at Bellevue Hospital. Fifteen (58%) presented with advanced disease (stage III or IV), including 7 (27%). All 26 patients required diagnostic work-up in the USA, and all 19 (73.1%) patients with stage 0-III disease underwent surgical excision. Patients from LMICs frequently present with advanced disease and in varying stages of breast cancer treatment. Improving communication with previous providers and fostering a collaborative approach with the international community are essential to developing efficacious treatment plans and improving oncologic outcomes.

The cumulative total of persons forced to leave their country for fear of persecution or organized violence reached an unprecedented 24.5 million by the end of 2015. Providing equitable access to appropriate health services for these highly diverse newcomers poses challenges for receiving countries. In this case study, we illustrate the importance of translating epidemiology into policy to address the health needs of refugees by highlighting examples of what works as well as identifying important policy-relevant gaps in knowledge. First, we formed an international working group of epidemiologists and health services researchers to identify available literature on the intersection of epidemiology, policy, and refugee health. Second, we created a synopsis of findings to inform a recommendation for integration of policy and epidemiology to support refugee health in the United States and other high-income receiving countries. Third, we identified eight key areas to guide the involvement of epidemiologists in addressing refugee health concerns. The complexity and uniqueness of refugee health issues, and the need to develop sustainable management information systems, require epidemiologists to expand their repertoire of skills to identify health patterns among arriving refugees, monitor access to appropriately designed health services, address inequities, and communicate with policy makers and multidisciplinary teams.

Using a retrospective cohort study design, we report empirical evidence on the effect of parental socioeconomic status, primary care, and health care expenditure associated with preterm or low-birth-weight (PLBW) babies on their mortality (neonatal, postneonatal, and under-5 mortality) under a universal health care system. A total of 4668 singleton PLBW babies born in Taiwan between January 1 and December 31, 2001, are extracted from a population-based medical claims database for a follow-up of up to 5 years. Multivariate survival models suggest the positive effect of higher parental income is significant in neonatal period but diminishes in later stages. Consistent inverse relationship is observed between adequate antenatal care and the three outcomes: neonatal hazard ratio (HR) = 0.494, 95% confidence interval (CI) = 0.312 to 0.783; postneonatal HR = 0.282, 95% CI = 0.102 to 0.774; and under-5 HR = 0.575, 95% CI = 0.386 to 0.857. Primary care services uptake should be actively promoted, particularly in lower income groups, to prevent premature PLBW mortality.

OBJECTIVE: Inequitable access to dental care contributes to oral health disparities. Midlevel dental provider models are utilized across the globe as a way to bridge the gap between preventive and restorative dental professionals and increase access to dental care. The purpose of this study was threefold: to examine lessons learned from the state legislative process related to creation of the hygienist-therapist in a Midwestern state, to improve understanding of the relationship between alternative oral health delivery models and public policy and to inform the development and passage of future policies aimed at addressing the unmet dental needs of the public.
METHODS: This research investigation utilized a qualitative research methodology to examine the process of legislation relating to an alternative oral health delivery model (hygienist-therapist) through the eyes of key stakeholders. Interview data was analyzed and then triangulated with 3 data sources: interviews with key stakeholders, documents and researcher participant field notes.
RESULTS: Data analysis resulted in consensus on 3 emergent themes with accompanying categories. The themes that emerged included social justice, partnerships and coalitions, and the legislative process.
CONCLUSIONS: This qualitative case study suggests that the creation of a new oral health workforce model was a long and arduous process involving multiple stakeholders and negotiation between the parties involved. The creation of this new workforce model was recognized as a necessary step to increasing access to dental care at the state and national level. The research in this case study may serve to inform advocates of access to oral health care as other states pursue their own workforce models.