Homelessness and Gender: Differences in Characteristics and Comorbidity of Substance Use Disorders at Admission to Services.

Background: Substance use disorders and homelessness are two closely related behavioral health issues. This study examines associations between homelessness and severity of substance use disorder (SUD) characteristics (inclusive of alcohol and other legal and illegal drugs) and presence of mental health comorbidity among individuals entering treatment for SUD. We also examine differences in SUD and mental health comorbidity by gender within a sample of clients experiencing homelessness upon admission to treatment. Methods: Using the 2017 Treatment Episodes Data Set, we used logistic regression models to examine the association between homelessness and indicators of severity (e.g., frequency of use, route of transmission) and the presence of mental health conditions, and to examine the association between gender and these dependent variables among only individuals experiencing homelessness at treatment admission. Results: After controlling for covariates, homelessness is significantly associated with increased odds of cocaine and methamphetamine use compared to all other substances, higher frequency of use, younger age of first use, use of more substances, injection drug use, and co-occurring mental health conditions. Female gender is inversely associated with younger age at first use and highly associated with co-occurring mental health conditions among those experiencing homelessness at admission to treatment. Conclusions: Individuals experiencing homelessness enter substance use services with different characteristics of substance use disorders, different patterns of use, and with higher rates of mental health comorbidity. Within homeless admissions, women have significant differences in substance use and higher likelihood of mental health comorbidity than men. Future research should focus on the specific treatment needs of individuals experiencing homelessness, and how the combination of homelessness and gender affects barriers and challenges to treatment.

Racial Differences in Self-Report of Mental Illness and Mental Illness Treatment in the Community: An Analysis of Jail Intake Data.

Jails and prisons in the United States house people with elevated rates of mental health and substance use disorders. The goal of this cross-sectional study was to evaluate the frequency of racial/ethnic differences in the self-report of mental illness and psychiatric medication use at jail entry. Our sample included individuals who had been incarcerated between 2016 and 2020 at the Middlesex Jail & House of Correction, located in Billerica, MA. We used data from the "Offender Management System," the administrative database used by the jail containing data on people who are incarcerated, and COREMR, the electronic medical record (EMR) used in the Middlesex Jail & House of Correction. We evaluated two primary outcomes (1) self-reported mental illness history and (2) self-reported use of psychiatric medication, with the primary indicator of interest as race/ethnicity. At intake, over half (57%) of the sample self-reported history of mental illness and 20% reported the use of psychiatric medications. Among people who self-reported a history of mental illness, Hispanic (AOR: 0.73, 95% CI: 0.60-0.90), Black (AOR: 0.52, 95% CI: 0.43-0.64), Asian/Pacific Islander (Non-Hispanic) people (AOR: 0.31, 95% CI: 0.13-0.74), and people from other racial/ethnic groups (AOR: 0.33, 95% CI: 0.11-0.93) all had decreased odds of reporting psychiatric medications. Mental illness was reported in about one-half of people who entered jail, but only 20% reported receiving medications in the community prior to incarceration. Our findings build on the existing literature on jail-based mental illness and show racial disparities in self-report of psychiatric medications in people who self-reported mental illness. The timing, frequency, and equity of mental health services in both the community and the jail setting deserves further research, investment, and improvement.

Associations among political voting preference, high-risk health status, and preventative behaviors for COVID-19.

BACKGROUND: We investigate the relationships among political preferences, risk for COVID-19 complications, and complying with preventative behaviors, such as social distancing, quarantine, and vaccination, as they remain incompletely understood. Since those with underlying health conditions have the highest mortality risk, prevention strategies targeting them and their caretakers effectively can save lives. Understanding caretakers' adherence is also crucial as their behavior affects the probability of transmission and quality of care, but is understudied. Examining the degree to which adherence to prevention measures within these populations is affected by their health status vs. voting preference, a key predictor of preventative behavior in the U. S, is imperative to improve targeted public health messaging. Knowledge of these associations could inform targeted COVID-19 campaigns to improve adherence for those at risk for severe consequences. METHODS: We conducted a nationally-representative online survey of U.S. adults between May-June 2020 assessing: 1) attempts to socially-distance; 2) willingness/ability to self-quarantine; and 3) intention of COVID-19 vaccination. We estimated the relationships between 1) political preferences 2) underlying health status, and 3) being a caretaker to someone with high-risk conditions and each dependent variable. Sensitivity analyses examined the associations between political preference and dependent variables among participants with high-risk conditions and/or obesity. RESULTS: Among 908 participants, 75.2% engaged in social-distancing, 94.4% were willing/able to self-quarantine, and 60.1% intended to get vaccinated. Compared to participants intending to vote for Biden, participants who intended to vote for Trump were significantly less likely to have tried to socially-distance, self-quarantine, or intend to be vaccinated. We observed the same trends in analyses restricted to participants with underlying health conditions and their caretakers Underlying health status was independently associated with social distancing among individuals with obesity and another high-risk condition, but not other outcomes. CONCLUSION: Engagement in preventative behavior is associated with political voting preference and not individual risk of severe COVID-19 or being a caretaker of a high-risk individual. Community based strategies and public health messaging should be tailored to individuals based on political preferences especially for those with obesity and other high-risk conditions. Efforts must be accompanied by broader public policy.

The importance of identification when measuring performance in addiction treatment.

Background: Identifying and effectively treating individuals with substance use disorders (SUDs) is an important priority for state Medicaid programs, given the enormous toll that SUDs take on individuals, their families, and their communities. In this paper, we describe how the Healthcare Effectiveness Data and Information Set (HEDIS) measure "Identification of Alcohol and Other Drug Services" can be used, along with eligible population prevalence rates, to expand states' ability to track how well their Medicaid programs identify enrollees with SUDs and link them with treatment (measured by initiation and engagement performance measures). Methods: We use the 2009 Medicaid MAX data on utilization and enrollment along with information from the National Survey of Drug Use and Health (NSDUH) to obtain state-level estimates of alcohol and drug abuse and dependence among Medicaid beneficiaries for 7 illustrative states. We calculate identification, initiation, and engagement measures using specifications from the National Committee on Quality Assurance (NCQA). Results: NSDUH data showed that the eligible population prevalence rate (the average rate of alcohol or drug abuse or dependence) among the 7 states was 10.0%, whereas the average identification rate was 2.9%. The gap between the prevalence and identification rates ranged from 5.1% to 11.0% among the 7 states. The initiation rates ranged from 36.9% to 57.1%. The states' engagement rates ranged from 11.8% to 31.1%, although rates differ by age, gender, and race/ethnicity in some states. Conclusion: Including identification along with initiation and engagement measures allows states to determine how well they are performing in a more complete spectrum from need, to recognition and documentation of enrollees with SUDs, to initiation of treatment, to continuation of early treatment.

Examining Racial and Ethnic Differences in Nursing Home Quality.

BACKGROUND: Identifying racial/ethnic differences in quality is central to identifying, monitoring, and reducing disparities. Although disparities across all individual nursing home residents and disparities associated with between-nursing home differences have been established, little is known about the degree to which quality of care varies by race//ethnicity within nursing homes. A study was conducted to measure within-facility differences for a range of publicly reported nursing home quality measures. METHODS: Resident assessment data on approximately 15,000 nursing homes and approximately 3 million residents (2009) were used to assess eight commonly used and publicly reported long-stay quality measures: the proportion of residents with weight loss, with high-risk and low-risk pressure ulcers, with incontinence, with depressive symptoms, in restraints daily, and who experienced a urinary tract infection or functional decline. Each measure was stratified by resident race/ethnicity (non-Hispanic white, non-Hispanic black, and Hispanic), and within-facility differences were examined. RESULTS: Small but significant differences in care on average were found, often in an unexpected direction; in many cases, white residents were experiencing poorer outcomes than black and Hispanic residents in the same facility. However, a broad range of differences in care by race/ethnicity within nursing homes was also found. CONCLUSION: The results suggest that care is delivered equally across all racial/ethnic groups in the same nursing home, on average. The results support the call for publicly reporting stratified nursing home quality measures and suggest that nursing home providers should attempt to identify racial/ethnic within-facility differences in care.

Admissions to detoxification after treatment: Does engagement make a difference?

BACKGROUND: Treatment engagement is a well-established performance measure for the treatment of substance use disorders. This study examined whether outpatient treatment engagement is associated with a reduced likelihood of subsequent detoxification admissions. METHODS: This study used administrative data on treatment services received by clients in specialty treatment facilities licensed in Massachusetts. The sample consisted of 11,591 adult clients who began an outpatient treatment episode in 2006. Treatment engagement was defined as receipt of at least 1 treatment service within 14 days of beginning a new outpatient treatment episode and receipt of at least 2 additional treatment services in the next 30 days. The outcome was a subsequent detoxification admission. Multilevel survival models examined the relationship between engagement and outcomes, with time to detoxification admission as the dependent variable censored at 365 days. RESULTS: Only 35% of clients met the outpatient engagement criteria, and 15% of clients had a detoxification admission within a year after beginning their outpatient treatment episode. Controlling for client demographics, insurance type, and substance use severity, clients who met the engagement criteria had a lower hazard of having a detoxification admission during the year following the index outpatient visit than those who did not engage (hazard ratio = 0.87, P < .01). CONCLUSIONS: Treatment engagement is a useful measure for monitoring quality of care. The findings from this study could help inform providers and policy makers on ways to target care and reduce the likelihood of more intensive services.

Race/ethnicity and quality indicators for outpatient treatment for substance use disorders.

BACKGROUND AND OBJECTIVES: Initiation and engagement, performance measures that focus on the frequency and timely receipt of services in the early stages of substance use disorders (SUD) treatment, are useful tools for assessing treatment quality differences across racial/ethnic groups. The purpose of this study was to examine whether there are racial/ethnic disparities in these quality indicators and to explore whether predictors of treatment initiation and engagement differ by clients' race/ethnicity. METHODS: This study used administrative data from outpatient treatment facilities licensed by the state of Massachusetts that receive public funding. The sample consisted of 10,666 adult clients (76% White, 13% Latino, 11% Black) who began an outpatient treatment episode in 2006. Client data were linked with facility data from the National Survey on Substance Abuse Treatment Services. Multilevel regressions were used to examine racial/ethnic disparities and to explore whether predictors for initiation and engagement differed by client's race/ethnicity. RESULTS: We did not find evidence of racial/ethnic disparities in treatment initiation or engagement. However, we found that predictors of initiation and engagement differed by client's race/ethnicity. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Disparities may be context specific, and thus it is important that they be examined at state or local levels. Our results point to the importance of examining predictors of quality indicators separately by group to better understand and address the needs of diverse client populations.

Psychosocial Stressors and Postpartum Depressive Symptoms Are Linked to Postpartum Contraceptive Use.

Background: Postpartum contraception plays a critical role in reducing the occurrence of rapid subsequent pregnancy, offering individuals reproductive choice, and promoting overall reproductive planning and well-being. In this study, we investigated the relationship between psychosocial stress during pregnancy, postpartum depressive symptoms (PDS), and postpartum contraceptive use. Materials and Methods: We analyzed data from the Pregnancy Risk Assessment Monitoring System (2012-2019), which included comprehensive information about maternal experiences, views, and needs before, during, and after pregnancy from four states and a large city, with a total sample size of N = 36,356. We conducted descriptive analyses as well as adjusted multivariable logistic regression models. Main Findings: Our findings demonstrate significant negative associations between partner-related (adjusted odds ratio [aOR] = 0.82, 95% confidence interval [95% CI]: 0.76-0.89, p ≤ 0.001) and trauma-related (aOR = 0.83, 95% CI: 0.75-0.92, p ≤ 0.001) stressors and postpartum contraceptive use. Additionally, we observed a negative association between PDS and postpartum contraceptive use (OR = 0.88, 95% CI: 0.80-0.97, p ≤ 0.01), indicating that individuals experiencing PDS are less likely to utilize contraception after giving birth. Furthermore, our study highlights racial/ethnic, socioeconomic, and parity postpartum contraceptive use disparities. Conclusions: Our findings emphasize the importance of incorporating psychosocial stressors and mental health into the promotion of effective postpartum contraception practices. These results have valuable implications for health care providers, policymakers, and researchers as they can guide the development of targeted interventions and support systems to contribute to improved reproductive health outcomes.

The 2023 Addiction Health Services Research Conference: Back in Person and Taking a Bite of the Big Apple.

The Addiction Health Services Research (AHSR) Conference has been held since 2002. This Conference brings together researchers, graduate students, policymakers, and treatment providers to focus improving the organization, distribution, and financing of healthcare resources for prevention/care of SUD. The AHSR 2023 Conference took place in New York City, October 18-20th, and was hosted by the Center for Health Economics of Treatment Interventions for Substance Use Disorder, HCV, and HIV (CHERISH; cherishresearch.org). Attended by more than 300 participants, the Conference comprised several themes relating to the latest research on addiction health services delivery, financing, and impact. The agenda also included pre-conference workshops, distinguished plenary speakers, a multitude of networking opportunities, and career support for early-stage and minority investigators. AHSR 2023 featured 3 plenary sessions, 120 oral presentations, and 143 poster presentations from academics throughout the world. Overall, AHSR 2023 provided numerous opportunities to advance the field of addiction health services research. The state-of-the-art techniques and insights gained by attending scholars will position them to be change-agents in the addiction field going forward.

Environmental equity and COVID-19 experiences in the United States: Results from three survey waves of a nationally representative study conducted between 2020-2022.

Certain environmental exposures, such as air pollution, are associated with COVID-19 incidence and mortality. To determine whether environmental context is associated with other COVID-19 experiences, we used data from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study data (n=1785; three survey waves 2020-2022). Environmental context was assessed using self-reported climate stress and county-level air pollution, greenness, toxic release inventory site, and heatwave data. Self-reported COVID-19 experiences included willingness to vaccinate against COVID-19, health impacts from COVID-19, receiving assistance for COVID-19, and provisioning assistance for COVID-19. Self-reported climate stress in 2020 or 2021 was associated with increased COVID-19 vaccination willingness by 2022 (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.47, 3.76), even after adjusting for political affiliation (OR = 1.79; 95% CI = 1.09, 2.93). Self-reported climate stress in 2020 was also associated with increased likelihood of receiving COVID-19 assistance by 2021 (OR = 1.89; 95% CI = 1.29, 2.78). County-level exposures (i.e., less greenness, more toxic release inventory sites, more heatwaves) were associated with increased vaccination willingness. Air pollution exposure in 2020 was positively associated with likelihood of provisioning COVID-19 assistance in 2020 (OR = 1.16 per µg/m3; 95% CI = 1.02, 1.32). Associations between certain environmental exposures and certain COVID-19 outcomes were stronger among those who identify as a race/ethnicity other than non-Hispanic White and among those who reported experiencing discrimination; however, these trends were not consistent. A latent variable representing a summary construct for environmental context was associated with COVID-19 vaccination willingness. Our results add to the growing body of literature suggesting that intersectional equity issues affecting likelihood of exposure to adverse environmental conditions are also associated with health-related outcomes.

Racial and ethnic differences in substance abuse treatment initiation and engagement.

This study examined variations by race and ethnicity in initiation and engagement, two performance measures of treatment for substance use disorders that focus on the timely receipt of services during the early stage of substance abuse treatment. Administrative data from the Oklahoma Department of Mental Health and Substance Abuse Services were linked with facility-level information from the National Survey of Substance Abuse Treatment Services. We found that Black clients were least likely to initiate treatment, but no race or ethnic differences in treatment engagement were found when compared by race or ethnicity. Most client and facility characteristics' association with initiation or engagement did not differ across racial or ethnic groups. Increased attention is needed to understand what may contribute to the differences and how to address them. This study also offers an approach that state agencies may implement for monitoring treatment quality and examining racial and ethnic disparities in substance abuse treatment services.

Experiences with and impacts of the COVID-19 pandemic by substance use disorder in the early phase of pandemic in the United States: A cross-sectional survey, 2020.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic could disproportionately affect individuals who have a substance use disorder (SUD). However, little information exists on COVID-19-related experiences among individuals with a SUD. We examined whether individuals with a SUD differ from other individuals with regard to COVID-19 testing, susceptibility, and employment-related vulnerability. METHODS: We used data from a U.S. nationally representative survey (n = 1,208). Using logistic regressions, we examined whether individuals with SUDs differ from other individuals regarding underlying health conditions, COVID-19 testing, access to paid sick leave, and loss of employment. Data were collected in late May-early June, 2020. RESULTS: Four percent of participants reported that a healthcare professional had told them they had a SUD. We found that, compared to those without SUDs, respondents with SUDs had higher odds of having lost their job due to the pandemic (adjusted odds ratio [AOR]:5.17, 95% confidence interval [CI]:2.28-11.74). Among individuals who were employed prior to the pandemic, people with SUDs had lower odds of having paid sick leave (AOR:0.26, 95% CI:0.09-0.74). CONCLUSION: Our study indicates that individuals with SUDs could be disproportionately affected by COVID-19 economically, which might worsen SUD and racial/ethnic health disparities.

Hospitalizations for Alcohol and Opioid Use Disorders in Older Adults: Trends, Comorbidities, and Differences by Gender, Race, and Ethnicity.

Background: The prevalence of substance use disorders (SUDs) among adults ages 65 and older has been increasing at a notably high rate in recent years, yet little information exists on hospitalizations for SUDs among this age group. In this study we examined trends in hospitalizations for alcohol use disorders (AUDs) and opioid use disorders (OUDs) among adults 65 and older in the United States, including differences by gender and race/ethnicity. Methods: We used Medicare claims data for years 2007-2014 from beneficiaries ages 65 and older. We abstracted hospitalization records with an ICD-9 diagnostic code for an AUD or OUD. Hospitalization rates were calculated using population estimates from the United States Census. We examined trends in quarterly hospitalization rates for hospitalizations with AUD/OUD as primary diagnoses, and separately for those with these disorders as secondary diagnoses. We also examined comorbidities for those with a primary diagnosis of AUD/OUD. Analyses were conducted for all hospitalizations with AUD/OUD diagnoses, and separately by gender and race/ethnicity. Results: Between the last quarter of 2007 and the third quarter of 2014, AUD hospitalization rates increased from 485 to 579 per million (19%), and OUD hospitalization rates from 46 to 101 per million (120%) and varied by gender (for AUD) and race/ethnicity (for both AUD and OUD). Hospitalization rates were particularly high for Black older adults, as was the increase in hospitalization rates. The increase in hospitalization rates was substantially higher for hospitalizations with AUD (84%) and OUD (269%) as secondary diagnoses. Conclusions: Hospitalizations for AUDs and OUDs among older adults increased at an alarming rate during the observation period, and disparities existed in hospitalization rates for these conditions. Interventions focusing on the needs of older adults with AUD and/or OUD are needed, particularly to address the needs of a growing racially/ethnically diverse older adult population.

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value.

AIMS: Atypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype. METHODS: Clinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry. RESULTS: Neutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells. CONCLUSION: We found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.

BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence decision support systems hold substantial promise as part of clinical practise in detecting haematological malignancy. This study aims to develop a deep learning-based system to predict the diagnosis of acute leukaemia using blood cell images. METHODS: A set of 731 blood smears containing 16,450 single-cell images was analysed from 100 healthy controls, 191 patients with viral infections and 148 with acute leukaemia. Training and testing sets were arranged with 85% and 15% of these smears, respectively. To find the best architecture for acute leukaemia classification VGG16, ResNet101, DenseNet121 and SENet154 were evaluated. Fine-tuning was implemented to these pre-trained CNNs to adapt their layers to our data. Once the best architecture was chosen, a system with two modules working sequentially was configured (ALNet). The first module recognised abnormal promyelocytes among other mononuclear blood cell images, such as lymphocytes, monocytes, reactive lymphocytes and blasts. The second distinguished if blasts were myeloid or lymphoid lineage. The final strategy was to predict patients' initial diagnosis of acute leukaemia lineage using the blood smear review. ALNet was assessed with smears of the testing set. RESULTS: ALNet provided the correct diagnostic prediction of all patients with promyelocytic and myeloid leukaemia. Sensitivity, specificity and precision values of 100%, 92.3% and 93.7%, respectively, were obtained for myeloid leukaemia. Regarding lymphoid leukaemia, a sensitivity of 89% and specificity and precision values of 100% were obtained. CONCLUSIONS: ALNet is a predictive model designed with two serially connected convolutional networks. It is proposed to assist clinical pathologists in the diagnosis of acute leukaemia during the blood smear review. It has been proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage.

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks.

Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the recognition of malaria has not been considered. We have developed the first deep learning model using convolutional neural networks capable of differentiating malaria-infected red blood cells from not only normal erythrocytes but also erythrocytes with other types of inclusions. 6415 images of red blood cells were segmented from digital images of 53 peripheral blood smears using thresholding and watershed transformation techniques. These images were used to train a VGG-16 architecture using transfer learning. Using an independent test set of 23 smears, this model was 99.5% accurate in classifying malaria parasites and other red blood cell inclusions. This model also exhibited sensitivity and specificity values of 100% and 91.7%, respectively, classifying a complete smear as infected or not infected. Our model represents a promising advance for automation in the identification of malaria-infected patients. The differentiation between malaria parasites and other red blood cell inclusions demonstrates the potential utility of our model in a real work environment.

A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes.

BACKGROUND: Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood. METHODS: Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%). RESULTS: We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%. CONCLUSIONS: The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.

Impact of the Medicare Shared Savings Program on utilization of mental health and substance use services by eligibility and race/ethnicity.

OBJECTIVE: To assess the impact of the Medicare Shared Savings Program (MSSP) ACOs on mental health and substance use services utilization and racial/ethnic disparities in care for these conditions. DATA SOURCES: Five percent random sample of Medicare claims from 2009 to 2016. STUDY DESIGN: We compared Medicare beneficiaries in MSSP ACOs to non-MSSP beneficiaries, stratifying analyses by Medicare eligibility (disability vs age 65+). We estimated difference-in-difference models of MSSP ACOs on mental health and substance use visits (outpatient and inpatient), medication fills, and adequate care for depression adjusting for age, sex, race/ethnicity, region, and chronic medical and behavioral health conditions. To examine the differential impact of MSSP on our outcomes by race/ethnicity, we used a difference-in-difference-in-differences (DDD) design. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: MSSP ACOs were associated with small reductions in outpatient mental health (Coeff: -0.012, P < .001) and substance use (Coeff: -0.001, P < .01) visits in the disability population, and in adequate care for depression for both the disability- and age-eligible populations (Coeff: -0.028, P < .001; Coeff: -0.012, P < .001, respectively). MSSP ACO's were also associated with increases in psychotropic medications (Coeff: 0.007 and Coeff: 0.0213, for disability- and age-eligible populations, respectively, both P < .001) and reductions in inpatient mental health stays (Coeff:-0.004, P < .001, and Coeff:-0.0002, P < .01 for disability- and age-eligible populations, respectively) and substance use-related stays for disability-eligible populations (Coeff:-0.0005, P<.05). The MSSP effect on disparities varied depending on type of service. CONCLUSIONS: We found small reductions in outpatient and inpatient stays and in rates of adequate care for depression associated with MSSP ACOs. As MSSP ACOs are placed at more financial risk for population-based treatment, it will be important to include more robust behavioral health quality measures in their contracts and to monitor disparities in care.

Why are some people reluctant to be vaccinated for COVID-19? A cross-sectional survey among U.S. Adults in May-June 2020.

Understanding reasons for COVID-19 vaccine hesitancy is necessary to ensure maximum uptake, needed for herd immunity. We conducted a cross-sectional online survey between May 29-June 20, 2020 among a national sample of U.S. adults ages 18 years and over to assess cognitive, attitudinal and normative beliefs associated with not intending to get a COVID-19 vaccine. Of 1219 respondents, 17.7% said that they would not get a vaccine and 24.2% were unsure. In multivariable analyses controlled for gender, age, income, education, religious affiliation, health insurance coverage, and political party affiliation, those who reported that they were unwilling be vaccinated (versus those who were willing) were less likely to agree that vaccines are safe/effective (Relative Risk Ratio (RRR): 0.45, 95% confidence interval (CI): 0.31, 0.66), that everyone has a responsibility to be vaccinated (RRR: 0.39, 95% CI: 0.30, 0.52), that public authorities should be able to mandate vaccination (RRR: 0.75, 95% CI: 0.58, 0.98), and more likely to believe that if everyone else were vaccinated they would not need a vaccine (RRR: 1.36, 95% CI: 1.04, 1.78). Our results suggest that health messages should emphasize the safety and efficacy of vaccines, as well as the fact that vaccinating oneself is important, even if the level of uptake in the community is high.

Barriers, facilitators, and disparities in retention for adolescents in treatment for substance use disorders: a qualitative study with treatment providers.

BACKGROUND: Retention in substance use treatment is one of the strongest predictors of improved outcomes among adolescents, making retention an important goal of treatment. We examined treatment providers' perspectives on barriers and facilitators to treatment retention among adolescents, and their views on contributors to racial/ethnic disparities in retention including ways to address disparities. METHODS: Semi-structured interviews were conducted with 19 providers at state-licensed detoxification, residential, and outpatient facilities serving adolescents for substance use disorders in Massachusetts. Interviews were coded by at least two independent coders. RESULTS: Providers identified barriers and facilitators at the policy/systems, facility, family, and client levels. Some of the barriers included insurance limits on sessions/length of stay and low reimbursement (policy/systems), staff turnover (facility), low family engagement (family), and low internal motivation (client). Some facilitators mentioned were support from state's substance use agency (policy/systems), flexibility with meeting location (facility), family participation (family), and high internal motivation and presence of external motivators (client). Barriers that contributed to racial/ethnic disparities included lower socio-economic status, language barriers, and mistrust. Having bilingual/bicultural staff and multi-lingual materials, and facilitating transportation were identified as strategies for reducing disparities in treatment retention. CONCLUSIONS: It is critical that adolescents who access substance use services remain and complete treatment and that there is equity in treatment retention. Provider perspectives in factors associated with retention can inform the development of comprehensive interventions and policies to help improve retention and reduce disparities.