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Background The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage. Methods This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. Results On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Conclusions Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Subtypes of suicide decedents have not been studied at a population level using linked clinical and public health surveillance records. Identifying suicide subtypes can help facilitate the development and deployment of population-level prevention strategies. This retrospective study uses the Maryland Suicide Data Warehouse (MSDW). The analyses included 848 individuals who died by suicide as well as 4,161 individuals who died by accident in the state of Maryland between January 1st, 2016, and December 31st, 2019. These individuals had electronic health records from Johns Hopkins Medical Institutes and statewide hospital discharge data. We employed deep embedded clustering and evaluated its performance against traditional clustering approaches. We evaluated different numbers of clusters ( k  = 2 to 10) and assessed clustering performance using stability metrics, achieving a cross-validated prediction strength of 0.94. We then performed cluster characterization and assessed cluster stability up to 1 year before suicide death. We identified four distinct suicide profiles. Profile 1 (23.2% of suicide cases) included older individuals with high comorbid conditions. Profile 2 (19.2%) was characterized by psychiatric illness, the highest healthcare utilization, and significant social needs. Profile 3 (25.4%) consisted of younger individuals with psychiatric illness, no recorded social needs, and the highest percentage of Medicaid patients. Profile 4 (32.2%) included less clinically engaged individuals with the fewest healthcare visits. Our findings show the effective use of clustering methods to identify meaningful and stable suicide decedent profiles, revealing significant demographic and clinical differences. The identified subtypes can inform population-level suicide prevention strategies.

Although the use of telehealth has declined since the pandemic, it remains a popular mode of care delivery across the cancer care continuum. Understanding telehealth in the context of cancer care is essential, as its benefits and challenges may differ among diverse population groups and geographic areas. This study aimed to examine patterns of telehealth utilization across the cancer care continuum and to identify factors associated with the receipt of telehealth in a large patient population. This study also aimed to assess the telehealth's impact on key health care delivery outcomes. We used an annualized retrospective cohort design using patient data from the Johns Hopkins Health System (JHHS), a large regional academic health center in Maryland. The study analyzed electronic health record (EHR) data covering the period from January 1, 2019, to December 31, 2023. Chronic conditions were defined through the Johns Hopkins Adjusted Clinical Groups (ACG) System, which identifies comorbidities based on the International Classification of Diseases, Tenth Revision, Clinical Modification, codes in the electronic health record. In addition, we used publicly available geospatial data (eg, internet connectivity, rural-urban commuting area) to assess telehealth receipt associations. Statistical modeling, including generalized estimating equations, was used to evaluate variations in telehealth utilization and outcomes. A total of 124,974 adult patients receiving cancer-related care at Johns Hopkins Health System were identified during the study period. Telehealth users were significantly older (52.2% aged ≥65 years, 19,942 patients) compared to nonusers (48.7%, 42,209 patients). In addition, these users were more likely to be male (45.4%, 17,365 patients vs 40.2%, 34,839 patients) and to identify as White (70.8%, 27,071 patients vs 64.7%, 56,122 patients). Telehealth users also had a higher prevalence of comorbidities, with 61.5% (23,503 patients) reporting 3 or more chronic conditions compared to 38.0% (33,000 patients) among nonusers. A positive correlation was noted between rural-urban commuting area codes and telehealth service utilization (ρ=0.36; P<0.05), indicating higher usage in more rural areas. Conversely, average maximum download and upload speeds showed an inverse relationship with telehealth utilization (ρ=-0.22; P<0.05; and ρ=-0.34; P<0.05, respectively). Adjusted analyses indicated that concurrent telehealth use was associated with reduced odds of emergency department visits (0.916, 95% CI 0.884-0.948) and hospitalizations (0.830, 95% CI 0.799-0.863), acknowledging the potential influence of residual confounding. Telehealth has emerged as a crucial mode of care delivery for patients with complex conditions such as cancer. Understanding usage patterns and factors influencing telehealth across the cancer care continuum, including geographic barriers, is vital to optimizing its implementation and ensuring health care systems meet the diverse needs of patients with cancer in a value-based care environment.

Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs). This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs. The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system. This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits. The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings. ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699. DERR1-10.2196/57316.

Background: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S. Objectives: To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions. Method: Kendall's Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation. Results: Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type. Conclusions: Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions.

People with schizophrenia are 3–4 times more likely to die from cardiovascular disease than the general population. Clozapine (CLZ) is the gold standard of treatment for refractory schizophrenia. It has been associated with tachycardia and recent evidence shows individuals prescribed CLZ may develop blood pressure (BP) elevation and hypertension. The purpose of this study was to examine the effects of CLZ on BP and heart rate (HR). This was a retrospective chart review of patients 18–75 years old with a DSM IV diagnosis of Schizophrenia or Schizoaffective disorder. Primary outcomes were systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR measured 12 weeks before and 24 weeks during CLZ treatment. Eighteen patient records were included in this study. The mean stabilized CLZ dose was 441.7 ± 171.8 mg/day. DBP (t = 1.02, df = 79.5, = 2.00, 0.049) and HR (t = 1.32, df = 355  = −4.61, < 0.0001) were significantly higher after CLZ initiation. A trend was noted for increase in SBP (p = 0.071). 22 % of patients met criteria for hypertension before CLZ and 67 % during CLZ treatment (Chi Square = 6.25, df = 1, p = 0.0124). No significant changes in weight or renal function occured during CLZ treatment. No patients had evidence of cardiomyopathy. The data suggest CLZ may be associated with a rise in BP and HR. The results of this study support previous literature that found an increase in SBP/DBP regardless of CLZ dose, occurring early in treatment. Due to high risk of cardiovascular morbidity and mortality, more work is needed to determine risk factors and understand the mechanism of action that may cause this side effect.

We investigated the role of both individual-level social needs and community-level social determinants of health (SDOH) in explaining emergency department (ED) utilization rates. We also assessed the potential synergies between the two levels of analysis and their combined effect on patterns of ED visits. We extracted electronic health record (EHR) data between July 2016 and June 2020 for 1,308,598 unique Maryland residents who received care at Johns Hopkins Health System, of which 28,937 (2.2%) patients had at least one documented social need. There was a negative correlation between median household income in a neighborhood with having a social need such as financial resource strain, food insecurity, and residential instability (correlation coefficient: -0.05, -0.01, and − 0.06, p = 0, respectively). In a multilevel model with random effects after adjusting for other factors, living in a more disadvantaged neighborhood was found to be significantly associated with ED utilization statewide and within Baltimore City (OR: 1.005, 95% CI: 1.003–1.007 and 1.020, 95% CI: 1.017–1.022, respectively). However, individual-level social needs appeared to enhance the statewide effect of living in a more disadvantaged neighborhood with the OR for the interaction term between social needs and SDOH being larger, and more positive, than SDOH alone (OR: 1.012, 95% CI: 1.011–1.014). No such moderation was found in Baltimore City. To our knowledge, this study is one of the first attempts by a major academic healthcare system to assess the combined impact of patient-level social needs in association with community-level SDOH on healthcare utilization and can serve as a baseline for future studies using EHR data linked to population-level data to assess such synergistic association.

Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the “clinical whitespace” between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.