Evaluation of patient health outcomes of a student-run free clinic in East Harlem.

BACKGROUND: Most United States medical schools have affiliated student-run free clinics, but the quality of services provided in such contexts compared to national metrics is unknown. This study determines whether a student-run, attending-supervised free clinic servicing a low-income and minority race patient population in New York City can meet national metrics of care. METHODS: Through chart review from January 1, 2020 to December 31, 2020, patient outcomes and service utilization in the Healthcare Effectiveness Data and Information Set were examined and compared to national rates of patients using Medicaid HMO or Medicare. Patients are ≥ 21 years of age, residents of East Harlem, and ineligible for health insurance because of legal residency requirements. The majority identify as Hispanic and speak Spanish as their primary language. All patients who were seen in the clinic during the 2020 calendar year were included. The primary study outcome is the number of Healthcare Effectiveness Data and Information Set measures in which patients, seen in a student-run free clinic, meet or exceed national comparisons. RESULTS: The healthcare outcomes of 238 patients, mean age 47.8 years and 54.6% female, were examined in 18 Healthcare Effectiveness Data and Information Set measures. The student-run free clinic met or exceeded national metrics in 16 out of 18 categories. CONCLUSIONS: The student-run free clinic met or exceeded the national standard of care according to national metrics. Evidence-based priorities have been clarified for future improvement. Other student-run free clinics should similarly evaluate the quality of their services.

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis.

BACKGROUND AND OBJECTIVES: Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS: We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION: A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.

The Association Between Moderate and Serious Mental Health Distress and General Health Services Utilization Among Chinese, Filipino, Japanese, Korean, and Vietnamese Adults in California.

INTRODUCTION: A growing body of literature has indicated that disaggregated analyses using distinct Asian subgroups allow for identification of varying mental health challenges and health services utilization. In this study, we examined the associations between distress and health services utilization among five Asian subgroups: Chinese, Korean, Japanese, Filipino, and Vietnamese adults in California. MATERIALS AND METHODS: Using a combined dataset using the 2011-2018 cross-sectional cycles of the California Health Interview survey, we assessed moderate and serious distress and four health services utilization indicators in a set of disaggregated analyses among adults 18 years of age and older in five Asian subgroups. We performed bivariate and multivariable analyses. RESULTS: The prevalence of and associations between moderate and serious distress and gaps in health services utilization varied among each Asian subgroup. Koreans had the highest prevalence of moderate and serious distress and the most gaps in health services utilization. Compared to those without moderate distress (p < .05), Japanese adults were more likely to delay care. Compared to those without serious distress (p < .05), Chinese adults who experienced serious distress were more likely to delay both medications and care, whereas Filipino and Vietnamese adults were more likely to delay medications. DISCUSSION: Disaggregating health data elucidates the impact of mental distress on healthcare-seeking behaviors among specific Asian subgroups. Identifying these influences can facilitate future tailored interventions, yet fully understanding the mechanism linking mental distress and healthcare usage will necessitate a comprehensive assessment of structural influences and Asian American experiences without otherization.

Death certificates compared to SEER-Medicare data for surveillance of liver cancer mortality due to hepatitis B or hepatitis C infection.

Hepatocellular carcinoma (HCC) is often caused by hepatitis B virus (HBV) or hepatitis C virus (HCV) infection. To investigate the completeness of death certificates for recording viral hepatitis in HCC death, we compared the proportion of HCC deaths with hepatitis virus infection reported on death certificates to that reported as claims in the Surveillance, Epidemiology, and End Results (SEER)-Medicare database among individuals ≥66 years of age. For 2001-2015, we tabulated proportions of HCC deaths with HBV or HCV infection in each database overall, and by demographic factors. To correct for under ascertainment of viral hepatitis-associated HCC on death certificates, we multiplied by the reciprocal ratio of death certificates to SEER-Medicare. Among HCC decedents, HBV infection was reported on 3.6% of death certificates and 17.2% of Medicare claims. For HCV, corresponding proportions were 14.9% and 26.9%. The ratio of HBV-attributable HCC deaths in death certificates to SEER-Medicare remained ~0.21 over time. The ratio of HCV-attributable HCC deaths decreased 22.1% per year, from 0.70 in 2001 to 0.37 in 2003, and increased 4.1% per year, from 0.47 in 2004 to 0.66 in 2015. Following correction, the 2015 mortality rate from death certificate data increased from 0.2 to 0.9 per 100,000 for HBV-attributable HCC and from 2.3 to 3.5 per 100,000 for HCV-attributable HCC. In conclusion, among older Americans dying from HCC, death certificates captured 21% of HBV and 55% of HCV infections compared to Medicare claims. Our results suggest that death certificates provide incomplete data for viral hepatitis-associated HCC surveillance.

Alcohol Consumption among Samoan Adults in 2010: Patterns, Correlates and Health Implications.

AIMS: We aim to describe alcohol consumption and related problems from a nationwide survey in 2010 in Samoa in association with sociodemographic variables as part of an intervention development. METHODS: The sample consisted of 3463 adults, 25-65 years of age. Participants self-reported alcohol consumption in the previous 12 months, patterns of drinking and alcohol-related psychosocial problems. Data about age, census region of residence, highest attained education level, employment, marital status, household assets score and current smoking status were gathered. RESULTS: More than one-third of men, 36.1%, and 4.1% of women consumed alcohol in the past year. There were greater proportions of alcohol users among younger adults, <45 years, in both men and women. Among men, being unemployed and residing outside of rural Savai'i and smoking cigarettes were associated with current alcohol use. Among women, tertiary education and cigarette smoking were strongly associated with alcohol use. Among alcohol consumers, almost 75% of both men and women reported being drunk more than once in the prior month, and 58% of men and 81% of women drank heavily, consuming >4 drinks for women and >5 drinks for men at least once per episode in the prior week. More men than women, 51% versus 26%, felt that alcohol consumption had interfered with their daily life. CONCLUSION: Our analyses identified correlates of alcohol consumption and associated problems that can help guide the development of targeted interventions for different sex and age groups to mitigate the social and physiological harms of alcohol misuse.