Publications by authors named "Andrea Acevedo"

43 Publications

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

Comput Biol Med 2021 Sep 22;136:104680. Epub 2021 Jul 22.

Biochemistry and Molecular Genetics Department, Biomedical Diagnostic Center, Hospital Clinic of Barcelona, Barcelona, Catalonia, Spain.

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.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104680DOI Listing
September 2021

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

Prev Med Rep 2021 Dec 14;24:101494. Epub 2021 Jul 14.

Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA.

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.
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http://dx.doi.org/10.1016/j.pmedr.2021.101494DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277541PMC
December 2021

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

Comput Biol Med 2021 07 11;134:104479. Epub 2021 May 11.

Department of Mathematics, Technical University of Catalonia, Barcelona East Engineering School, Barcelona, Spain.

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.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104479DOI Listing
July 2021

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

Comput Methods Programs Biomed 2021 Apr 12;202:105999. Epub 2021 Feb 12.

Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Spain.

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.
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http://dx.doi.org/10.1016/j.cmpb.2021.105999DOI Listing
April 2021

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

Health Serv Res 2021 Aug 5;56(4):581-591. Epub 2021 Feb 5.

Health Equity Research Lab, Department of Psychiatry, Cambridge Health Alliance, Cambridge, Massachusetts, USA.

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.
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http://dx.doi.org/10.1111/1475-6773.13625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313953PMC
August 2021

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

J Clin Pathol 2020 Dec 11. Epub 2020 Dec 11.

Department of Mathematics, Universitat Politecnica de Catalunya, Barcelona, Spain.

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 , thus explaining the better outcome of patients showing these cells circulating in blood.
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http://dx.doi.org/10.1136/jclinpath-2020-207087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735067PMC
December 2020

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

Subst Abuse Treat Prev Policy 2020 06 18;15(1):42. Epub 2020 Jun 18.

Eliot-Pearson Department of Child Study and Human Development, Tufts University, Medford, USA.

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.
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http://dx.doi.org/10.1186/s13011-020-00284-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302144PMC
June 2020

A dataset of microscopic peripheral blood cell images for development of automatic recognition systems.

Data Brief 2020 Jun 8;30:105474. Epub 2020 Apr 8.

Department of Mathematics. Technical University of Catalonia. Barcelona East Engineering School, Spain.

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.
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http://dx.doi.org/10.1016/j.dib.2020.105474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182702PMC
June 2020

Sequential classification system for recognition of malaria infection using peripheral blood cell images.

J Clin Pathol 2020 Oct 16;73(10):665-670. Epub 2020 Mar 16.

Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain.

Aims: Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells.

Methods: A total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system's recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis.

Results: The selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively.

Conclusions: The proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions.
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http://dx.doi.org/10.1136/jclinpath-2019-206419DOI Listing
October 2020

Spatiotemporal and Demographic Trends and Disparities in Cardiovascular Disease Among Older Adults in the United States Based on 181 Million Hospitalization Records.

J Am Heart Assoc 2019 11 29;8(21):e012727. Epub 2019 Oct 29.

Tufts Friedman School of Nutrition Science & Policy Boston MA.

Background The US population is aging, with concurrent increases in cardiovascular disease (CVD) burdens; however, spatiotemporal and demographic trends in CVD incidence in the US elderly have not been investigated in detail. This study aims to characterize trends from 1991 to 2014 in CVD hospitalizations among US Medicare beneficiaries, aged 65+ years, by single year of age/sex/race/state using records from the US Centers for Medicare & Medicaid, covering 98% of older Americans. Methods and Results We abstracted 181 202 758 US Centers for Medicare & Medicaid hospitalization records indicating CVD in any of 10 diagnosis codes; tabulated total cases of CVD by sex, age, race, state, and calendar year (1991-2014); and normalized hospitalization counts to standardize over data batches. Stratum-specific hospitalization rates were calculated using US Centers for Medicare & Medicaid records and US Census population counts; a cubic polynomial function was fit to year-specific distributions of rates by single year of age. Nationwide, CVD-related hospitalization rates increased from 1991 to 2014. Differences between hospitalization rates at age 65 and 66 years, representing magnitude of healthcare deferral until Medicare onset, increased by 7.49 per 100 people 1991 to 2006 overall, and were largest among blacks and Native Americans. Rates of CVD hospitalizations were consistently highest in the Midwest/Deep South. Evidence of misclassification of race/ethnicity in US Centers for Medicare & Medicaid hospitalization records in the 1990s was noted. Conclusions Trends in CVD-related hospitalization rates among older Americans highlight the essential need for targeted policies to reduce CVD burdens, to improve reporting of race/ethnicity in large administrative databases, and to enhance access to affordable healthcare.
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http://dx.doi.org/10.1161/JAHA.119.012727DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6898811PMC
November 2019

Race/Ethnicity, Community of Residence, and DUI Arrest After Beginning Treatment for an Alcohol Use Disorder.

J Behav Health Serv Res 2020 04;47(2):201-215

Department of Community Health, Tufts University, 574 Boston Avenue, Suite 208, Medford, MA, 02155, USA.

The purpose of this study was to examine whether racial/ethnic disparities in post-treatment arrests for driving under the influence (DUI) exist among clients receiving outpatient treatment for an alcohol use disorder (AUD) and to assess whether community characteristics were associated with this outcome. The sample included adults with an AUD entering publicly funded outpatient treatment in Washington State in 2012. Treatment data were linked with criminal justice and US Census data. Multilevel time-to-event analysis was employed to answer the research questions. Key independent variables included client race/ethnicity, community-level economic disadvantage, and racial/ethnic composition of the community. Latino clients and clients residing in communities with a higher proportion of Black residents had higher hazards of a DUI arrest post-treatment admission. Future research should examine whether disparities in DUI arrests are related to differences in treatment effectiveness or other factors (e.g., inequities in law enforcement) so that these disparities can be addressed.
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http://dx.doi.org/10.1007/s11414-019-09672-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042035PMC
April 2020

Recognition of peripheral blood cell images using convolutional neural networks.

Comput Methods Programs Biomed 2019 Oct 9;180:105020. Epub 2019 Aug 9.

Department of Mathematics Technical University of Catalonia Barcelona East Engineering School, Spain.

Background And Objectives: Morphological analysis is the starting point for the diagnostic approach of more than 80% of hematological diseases. However, the morphological differentiation among different types of normal and abnormal peripheral blood cells is a difficult task that requires experience and skills. Therefore, the paper proposes a system for the automatic classification of eight groups of peripheral blood cells with high accuracy by means of a transfer learning approach using convolutional neural networks. With this new approach, it is not necessary to implement image segmentation, the feature extraction becomes automatic and existing models can be fine-tuned to obtain specific classifiers.

Methods: A dataset of 17,092 images of eight classes of normal peripheral blood cells was acquired using the CellaVision DM96 analyzer. All images were identified by pathologists as the ground truth to train a model to classify different cell types: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (myelocytes, metamyelocytes and promyelocytes), erythroblasts and platelets. Two designs were performed based on two architectures of convolutional neural networks, Vgg-16 and Inceptionv3. In the first case, the networks were used as feature extractors and these features were used to train a support vector machine classifier. In the second case, the same networks were fine-tuned with our dataset to obtain two end-to-end models for classification of the eight classes of blood cells.

Results: In the first case, the experimental test accuracies obtained were 86% and 90% when extracting features with Vgg-16 and Inceptionv3, respectively. On the other hand, in the fine-tuning experiment, global accuracy values of 96% and 95% were obtained using Vgg-16 and Inceptionv3, respectively. All the models were trained and tested using Keras and Tensorflow with a Nvidia Titan XP Graphics Processing Unit.

Conclusions: The main contribution of this paper is a classification scheme involving a convolutional neural network trained to discriminate among eight classes of cells circulating in peripheral blood. Starting from a state-of-the-art general architecture, we have established a fine-tuning procedure to develop an end-to-end classifier trained using a dataset with over 17,000 cell images obtained from clinical practice. The performance obtained when testing the system has been truly satisfactory, the values of precision, sensitivity, and specificity being excellent. To summarize, the best overall classification accuracy has been 96.2%.
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http://dx.doi.org/10.1016/j.cmpb.2019.105020DOI Listing
October 2019

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

J Clin Pathol 2019 Nov 29;72(11):755-761. Epub 2019 Jun 29.

Mathematics, EEBE, Technical University of Catalonia, Barcelona, Catalonia, Spain.

Aims: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images.

Methods: A set of 442 smears was analysed from 206 patients. It was split into a with 75% of these smears and a with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the . The final strategy was to predict the patient's diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the .

Results: The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis.

Conclusions: The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.
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http://dx.doi.org/10.1136/jclinpath-2019-205949DOI Listing
November 2019

Rural Clients' Continuity Into Follow-Up Substance Use Disorder Treatment: Impacts of Travel Time, Incentives, and Alerts.

J Rural Health 2020 03 15;36(2):196-207. Epub 2019 May 15.

Research and Data Analysis, Washington State Department of Social and Health Services, Olympia, Washington.

Purpose: Treatment after discharge from detoxification or residential treatment is associated with improved outcomes. We examined the influence of travel time on continuity into follow-up treatment and whether financial incentives and weekly alerts have a modifying effect.

Methods: For a research intervention during October 2013 to December 2015, detoxification and residential substance use disorder treatment programs in Washington State were randomized into 4 groups: potential financial incentives for meeting performance goals, weekly alerts to providers, both interventions, and control. Travel time was used as both a main effect and interacted with other variables to explore its modifying impact on continuity of care in conjunction with incentives or alerts. Continuity was defined as follow-up care occurring within 14 days of discharge from detoxification or residential treatment programs. Travel time was estimated as driving time from clients' home ZIP Code to treatment agency ZIP Code.

Findings: Travel times to the original treatment agency were in some cases significant with longer travel times predicting lower likelihood of continuity. For detoxification clients, those with longer travel times (over 91 minutes from their residence) are more likely to have timely continuity. Conversely, residential clients with travel times of more than 1 hour are less likely to have timely continuity. Interventions such as alerts or incentives for performance had some mitigating effects on these results. Travel times to the closest agency for potential further treatment were not significant.

Conclusions: Among rural clients discharged from detoxification and residential treatment, travel time can be an important factor in predicting timely continuity.
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http://dx.doi.org/10.1111/jrh.12375DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856385PMC
March 2020

Disparities in Criminal Justice Outcomes After Beginning Treatment for Substance Use Disorders: The Influence of Race/Ethnicity and Place.

J Stud Alcohol Drugs 2019 03;80(2):220-229

Institute for Behavioral Health, The Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts.

Objective: This study examined whether racial/ethnic disparities exist in posttreatment arrests and assessed the extent to which community characteristics account for such disparities.

Method: Administrative data on clients (N = 10,529) receiving publicly funded services in Washington State were linked with criminal justice and census data. Multilevel survival models were used for two outcomes measuring time (in days) to any arrest and to any substance-related arrest. Community characteristics included a factor measuring community economic disadvantage and the proportions of residents in the client's residential census tract who were Black, Latino, or American Indian/Alaskan Native.

Results: When we controlled for age, sex, substance use, referral source, and prior criminal justice involvement, Black clients (hazard ratio [HR] = 1.47, p < .01) had a higher hazard of any arrest compared with White clients, and Black (HR = 1.27, p < .05) and Latino (HR = 1.20, p < .05) clients had a higher hazard of a substance-related arrest. Clients living in census tracts with a higher proportion of Black residents had a higher hazard of any arrest (HR = 1.25, p < .01) as well as substance-related arrests (HR = 1.39, p < .01). Community characteristics did not account for racial/ethnic disparities in arrests but provided an independent effect.

Conclusions: Disparities in arrest outcomes are influenced by both individual- and community-level factors; therefore, strategies for reducing disparities in this treatment outcome should be implemented at both levels.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489550PMC
March 2019

Quantitative Cytologic Descriptors to Differentiate CLL, Sézary, Granular, and Villous Lymphocytes Through Image Analysis.

Am J Clin Pathol 2019 06;152(1):74-85

Department of Mathematics, Barcelona Est Engineering School, Technical University of Catalonia, Barcelona, Spain.

Objectives: We aimed to find descriptors to identify chronic lymphocytic leukemia (CLL), Sézary, granular, and villous lymphocytes among normal and abnormal lymphocytes in peripheral blood.

Methods: Image analysis was applied to 768 images from 15 different types of lymphoid cells and monocytes to determine four discriminant descriptors. For each descriptor, numerical scales were obtained using 627 images from 79 patients. An assessment of the four descriptors was performed using smears from 209 new patients.

Results: Cyan correlation of the nucleus identified clumped chromatin, and standard deviation of the granulometric curve of the cyan of the nucleus was specific for cerebriform chromatin. Skewness of the histogram of the u component of the cytoplasm identified cytoplasmic granulation. Hairiness showed specificity for cytoplasmic villi. In the assessment, 96% of the smears were correctly classified.

Conclusions: The quantitative descriptors obtained through image analysis may contribute to the morphologic identification of the abnormal lymphoid cells considered in this article.
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http://dx.doi.org/10.1093/ajcp/aqz025DOI Listing
June 2019

The importance of identification when measuring performance in addiction treatment.

Subst Abus 2019 26;40(3):263-267. Epub 2019 Mar 26.

Truven Health Analytics Inc., IBM Watson Health, Cambridge, Massachusetts, USA.

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). 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). 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. 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.
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http://dx.doi.org/10.1080/08897077.2019.1580240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763371PMC
July 2020

Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood.

Med Biol Eng Comput 2019 Jun 7;57(6):1265-1283. Epub 2019 Feb 7.

Department of Mathematics, EEBE, Technical University of Catalonia, Street Eduard Maristany 6-12, 08019, Barcelona, Spain.

Current computerized image systems are able to recognize normal blood cells in peripheral blood, but fail with abnormal cells like the classes of lymphocytes associated to lymphomas. The main challenge lies in the subtle differences in morphologic characteristics among these classes, which requires a refined segmentation. A new efficient segmentation framework has been developed, which uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers. The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. Segmentation of this zone is crucial to extract a new feature to identify cells with hair-like projections. The segmentation is validated, using a database of 4758 cell images with normal, reactive lymphocytes and five types of malignant lymphoid cells from blood smears of 105 patients, in two ways: (1) the efficiency in the accurate separation of the regions of interest, which is 92.24%, and (2) the accuracy of a classification system implemented over the segmented cells, which is 91.54%. In conclusion, the proposed segmentation framework is suitable to distinguish among abnormal blood cells with subtile color and spatial similarities. Graphical Abstract The segmentation framework uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers (Top). The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. The procedure is also validated by the implementation of a system to automatically classify different types of abnormal blood cells (Bottom).
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http://dx.doi.org/10.1007/s11517-019-01954-7DOI Listing
June 2019

Correction to: Disparities in the Treatment of Substance Use Disorders: Does Where You Live Matter?

J Behav Health Serv Res 2019 01;46(1):187

Behavioral Health Administration, Washington State Department of Social and Health Services, P.O. Box 45330, (MS: 45330), Olympia, WA, USA.

The professional degree of co-author Kevin Campbell is incorrect. It should be "DrPH" and not "PhD".
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http://dx.doi.org/10.1007/s11414-018-9640-9DOI Listing
January 2019

Impact of Agency Receipt of Incentives and Reminders on Engagement and Continuity of Care for Clients With Co-Occurring Disorders.

Psychiatr Serv 2018 07 26;69(7):804-811. Epub 2018 Apr 26.

With the exception of Dr. Campbell, the authors are with the Institute for Behavioral Health, Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts. Dr. Acevedo is also with the Department of Community Health, Tufts University, Medford, Massachusetts. Dr. Campbell is with the Division of Behavioral Health and Recovery, Washington State Behavioral Health Administration, Olympia.

Objective: This study examined whether having co-occurring substance use and mental disorders influenced treatment engagement or continuity of care and whether offering financial incentives, client-specific electronic reminders, or a combination to treatment agencies improved treatment engagement and continuity of care among clients with co-occurring disorders.

Methods: The study used a randomized cluster design to assign agencies (N=196) providing publicly funded substance use disorder treatment in Washington State to a research arm: incentives only, reminders only, incentives and reminders, and a control condition. Data were analyzed for 76,044 outpatient, 32,797 residential, and 39,006 detoxification admissions from Washington's treatment data system. Multilevel logistic regressions were conducted, with clients nested within agencies, to examine the effect of the interventions on treatment engagement and continuity of care.

Results: Compared with clients with a substance use disorder only, clients with co-occurring disorders were less likely to engage in outpatient treatment or have continuity of care after discharge from residential treatment, but they were more likely to have continuity of care after discharge from detoxification. The interventions did not influence treatment engagement or continuity of care, except the reminders had a positive impact on continuity of care after residential treatment among clients with co-occurring disorders.

Conclusions: In general, the interventions did not result in improved treatment engagement or continuity of care. The limited number of significant results supporting the influence of incentives and alerts on treatment engagement and continuity of care add to the mixed findings reported by previous research. Multiple interventions may be needed for performance improvement.
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http://dx.doi.org/10.1176/appi.ps.201700465DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193487PMC
July 2018

Improving Quality of Care for SUDS: Where Do We Go From Here?

Authors:
Andrea Acevedo

J Addict Med 2018 Jul/Aug;12(4):257-258

Department of Community Health, Tufts University, Medford, MA.

: Initiation and engagement (IET), a process quality indicator for the treatment for substance use disorders (SUDs), has been associated with better treatment outcomes and has been part of the Healthcare Effectiveness Data and Information Set for over a decade. However, nationally, IET rates tend to be low and not improving. Integration may be a promising way to improve IET and quality of care, as suggested by the findings. To guarantee that integration is a truly effective mechanism for improving patient engagement and quality would likely require providing clinicians and other primary care providers with additional support and training on SUDs and treatment, and ensure that everyone, regardless of demographic characteristics, can equally benefit from these system level changes.
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http://dx.doi.org/10.1097/ADM.0000000000000402DOI Listing
March 2020

Employment after beginning treatment for substance use disorders: The impact of race/ethnicity and client community of residence.

J Subst Abuse Treat 2018 04 11;87:31-41. Epub 2018 Jan 11.

Institute for Child, Youth, and Family Policy, Heller School for Social Policy and Management, Brandeis University, United States.

Employment is an important substance use treatment outcome, frequently used to assess individual progress during and after treatment. This study examined whether racial/ethnic disparities exist in employment after beginning treatment. It also examined the extent to which characteristics of clients' communities account for such disparities. Analyses are based on data that linked individual treatment information from Washington State's Behavioral Health Administration with employment data from the state's Employment Security Department. Analyses subsequently incorporated community-level data from the U.S. Census Bureau. The sample includes 10,636 adult clients (Whites, 68%; American Indians, 13%, Latinos, 10%; and Blacks, 8%) who had a new outpatient treatment admission to state-funded specialty treatment. Heckman models were used to test whether racial/ethnic disparities existed in the likelihood of post-admission employment, as well as employment duration and wages earned. Results indicated that there were no racial/ethnic disparities in the likelihood of employment in the year following treatment admission. However, compared to White clients, American Indian and Black clients had significantly shorter lengths of employment and Black clients had significantly lower wages. With few exceptions, residential community characteristics were associated with being employed after initiating treatment, but not with maintaining employment or with wages. After accounting for community-level variables, disparities in length of employment and earned wages persisted. These findings highlight the importance of considering the race/ethnicity of a client when examining post-treatment employment alongside community characteristics, and suggest that the effect of race/ethnicity and community characteristics on post-treatment employment may differ based on the stage of the employment process.
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http://dx.doi.org/10.1016/j.jsat.2018.01.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830150PMC
April 2018

Disparities in the Treatment of Substance Use Disorders: Does Where You Live Matter?

J Behav Health Serv Res 2018 10;45(4):533-549

Behavioral Health Administration, Washington State Department of Social and Health Services, P.O. Box 45330 (MS: 45330), Olympia, WA, 98504-5330, USA.

This study focused on (1) whether disparities in timely receipt of substance use services can be explained in part by the characteristics of the community in which the clients reside and (2) whether the effect of community characteristics on timely receipt of services was similar across racial/ethnic groups. The sample was composed of adults receiving publicly funded outpatient treatment in Washington State. Treatment data were linked to data from the US census. The outcome studied was "Initiation and Engagement" in treatment (IET), a measure noting timely receipt of services at the beginning of treatment. Community characteristics studied included community level economic disadvantage and concentration of American Indian, Latino, and Black residents in the community. Black and American Indian clients were less likely to initiate or engage in treatment compared to non-Latino white clients, and American Indian clients living in economically disadvantaged communities were at even greater risk of not initiating treatment. Community economic disadvantage and racial/ethnic makeup of the community were associated with treatment initiation, but not engagement, although they did not entirely explain the disparities found in IET.
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http://dx.doi.org/10.1007/s11414-018-9586-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6087681PMC
October 2018

Agency-level financial incentives and electronic reminders to improve continuity of care after discharge from residential treatment and detoxification.

Drug Alcohol Depend 2018 02 16;183:192-200. Epub 2017 Dec 16.

Thurston Mason Behavioral Health Organization, United States.

Background: Despite the importance of continuity of care after detoxification and residential treatment, many clients do not receive further treatment services after discharged. This study examined whether offering financial incentives and providing client-specific electronic reminders to treatment agencies lead to improved continuity of care after detoxification or residential treatment.

Methods: Residential (N = 33) and detoxification agencies (N = 12) receiving public funding in Washington State were randomized into receiving one, both, or none (control group) of the interventions. Agencies assigned to incentives arms could earn financial rewards based on their continuity of care rates relative to a benchmark or based on improvement. Agencies assigned to electronic reminders arms received weekly information on recently discharged clients who had not yet received follow-up treatment. Difference-in-difference regressions controlling for client and agency characteristics tested the effectiveness of these interventions on continuity of care.

Results: During the intervention period, 24,347 clients received detoxification services and 20,685 received residential treatment. Overall, neither financial incentives nor electronic reminders had an effect on the likelihood of continuity of care. The interventions did have an effect among residential treatment agencies which had higher continuity of care rates at baseline.

Conclusions: Implementation of agency-level financial incentives and electronic reminders did not result in improvements in continuity of care, except among higher performing agencies. Alternative strategies at the facility and systems levels should be explored to identify ways to increase continuity of care rates in specialty settings, especially for low performing agencies.
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http://dx.doi.org/10.1016/j.drugalcdep.2017.11.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5803317PMC
February 2018

Examining Racial and Ethnic Differences in Nursing Home Quality.

Jt Comm J Qual Patient Saf 2017 11 29;43(11):554-564. Epub 2017 Sep 29.

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.
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http://dx.doi.org/10.1016/j.jcjq.2017.06.003DOI Listing
November 2017

Influencing quality of outpatient SUD care: Implementation of alerts and incentives in Washington State.

J Subst Abuse Treat 2017 11 14;82:93-101. Epub 2017 Sep 14.

Thurston Mason Behavioral Health Organization, United States.

Financial incentives for quality improvement and feedback on specific clients are two approaches to improving the quality of treatment for individuals with substance use disorders. We examined the impacts of these interventions in Washington State by randomizing outpatient substance use treatment agencies into intervention and control groups. From October 2013 through December 2015, agencies could earn financial incentives for meeting performance goals incorporating both achievement relative to a benchmark and improvement from agencies' own baselines. Weekly feedback was e-mailed to agencies in the alert or alert plus incentives arms. Difference-in difference regressions controlling for client and agency characteristics showed that none of the interventions significantly affected client engagement after outpatient admissions, overall or for sub-groups based on race/ethnicity, age, rural residence, or agency baseline performance. Treatment agencies offered insights related to several themes: delivery system context (e.g., agency time and resources needed during transition to a managed behavioral healthcare system), implementation (e.g., data lag), agency issues (e.g., staff turnover), and client factors (e.g., motivation). Interventions took place during a time of Medicaid expansion and planning for statewide integration of mental health and substance use disorder treatment into a managed care model, which may have resulted in agencies not responding to the interventions. Moreover, incentives and alerts at the agency-level may not be effective when factors are at play beyond the agency's control.
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http://dx.doi.org/10.1016/j.jsat.2017.09.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5653287PMC
November 2017

New quantitative features for the morphological differentiation of abnormal lymphoid cell images from peripheral blood.

J Clin Pathol 2017 Dec 13;70(12):1038-1048. Epub 2017 Jun 13.

CoDAlab, Polytechnic University of Catalonia, Barcelona, Spain.

Aims: This work aims to propose a set of quantitative features through digital image analysis for significant morphological qualitative features of different cells for an objective discrimination among reactive, abnormal and blast lymphoid cells.

Methods: Abnormal lymphoid cells circulating in peripheral blood in chronic lymphocytic leukaemia, B-prolymphocytic leukaemia, hairy cell leukaemia, splenic marginal zone lymphoma, mantle cell lymphoma, follicular lymphoma, T-prolymphocytic leukaemia, T large granular lymphocytic leukaemia and Sézary syndrome, normal, reactive and blast lymphoid cells were included. From 325 patients, 12 574 cell images were obtained and 2676 features (27 geometric and 2649 related to colour and texture) were extracted and analysed.

Results: We analysed the 20 most relevant features for the morphological differentiation of the 12 lymphoid cell groups under study. Most of them showed significant differences: 19 comparing follicular and mantle cells, 18 for blast and reactive cells, 17 for Sézary cells and T prolymphocytes and 16 for B and T prolymphocytes and 16 for villous lymphocytes. Moreover, a total of five quantitative features were significant for the discrimination among reactive and the set of abnormal lymphoid cells included.

Conclusions: Image analysis may assist in quantifying cell morphology turning qualitative data into quantitative values. New cytological variables were established based on geometric and colour/texture features to contribute to a more accurate and objective morphological assessment of lymphoid cells and their association with flow cytometry methods may be interesting to explore in the next future.
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http://dx.doi.org/10.1136/jclinpath-2017-204389DOI Listing
December 2017

Reducing Behavioral Health Inpatient Readmissions for People With Substance Use Disorders: Do Follow-Up Services Matter?

Psychiatr Serv 2017 Aug 17;68(8):810-818. Epub 2017 Apr 17.

Dr. Reif, Dr. Acevedo, and Dr. Garnick are with the Institute for Behavioral Health, Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts. Dr. Acevedo is also with the Department of Community Health, Tufts University, Medford, Massachusetts. At the time of the study, Dr. Fullerton was with Truven Health Analytics, Cambridge, Massachusetts.

Objective: Individuals with substance use disorders are at high risk of hospital readmission. This study examined whether follow-up services received within 14 days of discharge from an inpatient hospital stay or residential detoxification reduced 90-day readmissions among Medicaid enrollees whose index admission included a substance use disorder diagnosis.

Methods: Claims data were analyzed for Medicaid enrollees ages 18-64 with a substance use disorder diagnosis coded in any position for an inpatient hospital stay or residential detoxification in 2008 (N=30,439). Follow-up behavioral health services included residential, intensive outpatient, outpatient, and medication-assisted treatment (MAT). Analyses included data from ten states or fewer, based on a minimum number of index admissions and the availability of follow-up services or MAT. Survival analyses with time-varying independent variables were used to test the association of receipt of follow-up services and MAT with behavioral health readmissions.

Results: Two-thirds (67.7%) of these enrollees received no follow-up services within 14 days. Twenty-nine percent were admitted with a primary behavioral health diagnosis within 90 days of discharge. Survival analyses showed that MAT and residential treatment were associated with reduced risk of 90-day behavioral health admission. Receipt of outpatient treatment was associated with increased readmission risk, and, in only one model, receipt of intensive outpatient services was also associated with increased risk.

Conclusions: Provision of MAT or residential treatment for substance use disorders after an inpatient or detoxification stay may help prevent readmissions. Medicaid programs should be encouraged to reduce barriers to MAT and residential treatment in order to prevent behavioral health admissions.
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http://dx.doi.org/10.1176/appi.ps.201600339DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5895963PMC
August 2017

Choosing a Nursing Home: What Do Consumers Want to Know, and Do Preferences Vary across Race/Ethnicity?

Health Serv Res 2016 06 11;51 Suppl 2:1167-87. Epub 2016 Feb 11.

Heller School of Social Policy and Management, Brandeis University, Waltham, MA.

Objective: To identify what consumers want to know about nursing homes (NHs) before choosing one and to determine whether information preferences vary across race/ethnicity.

Data Sources/study Setting: Primary data were collected in Greater Boston (January 2013-February 2014) from community-dwelling, white, black, and Latino adults aged 65+ and 40-64 years, who had personal/familial experience with a NH admission or concerns about one.

Study Design: Eleven focus groups and 30 interviews were conducted separately by race/ethnicity and age group.

Principal Findings: Participants wanted detailed information on the facility, policies, staff, and residents, such as location, staff treatment of residents, and resident conditions. They wanted a sense of the NH gestalt and were interested in feedback/reviews from residents/families. Black and Latino participants were especially interested in resident and staff racial/ethnic concordance and facility cultural sensitivity. Latino participants wanted information on staff and resident language concordance.

Conclusions: Consumers want more information about NHs than what is currently available from resources like Nursing Home Compare. Report card makers can use these results to enhance their websites, and they should consider the distinct needs of different racial/ethnic groups. Future research should test methods for collecting and reporting resident and family feedback/reviews.
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http://dx.doi.org/10.1111/1475-6773.12457DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874936PMC
June 2016

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

Subst Abus 2016 Apr-Jun;37(2):364-71. Epub 2015 Aug 26.

a Institute for Behavioral Health, The Heller School for Social Policy and Management, Brandeis University , Waltham , Massachusetts , USA.

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.
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http://dx.doi.org/10.1080/08897077.2015.1080784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769122PMC
January 2018
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