Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review

Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the same time, the expression of human language plays a central role in the detection of mental health problems. Whereas spoken language is implicitly assessed during interviews with patients, written language can also provide interesting insights to clinical professionals. Existing work in the field often investigates mental health problems such as depression or anxiety. However, there is also work investigating how the diagnostics of eating disorders can benefit from these novel technologies. In this paper, we present a systematic overview of the latest research in this field. Our investigation encompasses four key areas: (a) an analysis of the metadata from published papers, (b) an examination of the sizes and specific topics of the datasets employed, (c) a review of the application of machine learning techniques in detecting eating disorders from text, and finally (d) an evaluation of the models used, focusing on their performance, limitations, and the potential risks associated with current methodologies.

The Use of Natural Language Processing for Computer-Aided Diagnostics and Monitoring of Body Image Perception in Patients with Cancers

BACKGROUND

Head and neck cancers (H&NCs) constitute a significant part of all cancer cases. H&NC patients experience unintentional weight loss, poor nutritional status, or speech disorders. Medical interventions affect appearance and interfere with patients' self-perception of their bodies. Psychological consultations are not affordable due to limited time.

METHODS

We used NLP to analyze the basic emotion intensity, sentiment about one's body, characteristic vocabulary, and potential areas of difficulty in free notes. The emotion intensity research uses the extended NAWL dictionary developed using word embedding. The sentiment analysis used a hybrid approach: a sentiment dictionary and a deep recursive network. The part-of-speech tagging and domain rules defined by a psycho-oncologist determine the distinct language traits. Potential areas of difficulty were analyzed using the dictionaries method with word polarity to define a given area and the presentation of a note using bag-of-words. Here, we applied the LSA method using SVD to reduce dimensionality. A total of 50 cancer patients requiring enteral nutrition participated in the study.

RESULTS

The results confirmed the complexity of emotions in patients with H&NC in relation to their body image. A negative attitude towards body image was detected in most of the patients. The method presented in the study appeared to be effective in assessing body image perception disturbances, but it cannot be used as the sole indicator of body image perception issues.

LIMITATIONS

The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary.

CONCLUSIONS

The combination of the attributes of a patient's condition, possible to determine using the method for a specific patient, can indicate the direction of support for the patient, relatives, direct medical personnel, and psycho-oncologists.

Treatment characteristics among patients with binge-eating disorder: an electronic health records analysis

OBJECTIVES

Treatment for adults diagnosed with binge-eating disorder (BED) includes psychotherapy and/or pharmacotherapy and aims to reduce the frequency of binge-eating episodes and disordered eating, improve metabolic-related issues and reduce weight, and address mood symptoms. Data describing real-world treatment patterns are lacking; therefore, this study aims to characterize real-world treatment patterns among patients with BED.

METHODS

This retrospective study identified adult patients with BED using natural language processing of clinical notes from the Optum electronic health record database from 2009 to 2015. Treatment patterns were examined during the 12 months preceding the BED recognition date and during a follow-up period after BED recognition (1-3 years for most patients).

RESULTS

Among 1042 patients, 384 were categorized as the BED cohort and 658, who met less stringent criteria, were categorized as probable BED. In the BED cohort, mean ± SD age was 45.2 ± 13.4 years and 81.8% were women (probable BED, 45.9 ± 12.8 years, 80.2%). A greater percentage of patients in the BED cohort were prescribed pharmacotherapy (70.6% [probable BED, 66.9%]) than received/discussed psychotherapy (53.1% [probable BED, 39.2%]) at baseline. In the BED cohort, 54.4% of patients were prescribed antidepressants (probable BED, 52.4%), 25.3% stimulants (probable BED, 20.1%), and 34.4% nonspecific psychotherapy (probable BED, 24.6%) at baseline, with no substantive differences observed during follow-up. Low percentages of patients in the BED cohort received/discussed cognitive behavioral therapy at baseline (12.5% [probable BED, 9.0%) or during follow-up (13.0% [probable BED, 8.8%). Among patients with ≥1 psychotherapy visit, the mean ± SD number of visits in the BED cohort was 1.2 ± 5.9 at baseline (probable BED, 1.7 ± 7.3) and 2.2 ± 7.7 during follow-up (probable BED, 2.6 ± 7.7).

CONCLUSION

This cohort of patients with BED was treated more frequently with pharmacotherapy than psychotherapy. These data may help inform strategies for reducing differences between real-world treatment patterns and evidence-based recommendations.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

OBJECTIVE

Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.

MATERIALS AND METHODS

A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.

RESULTS

Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).

CONCLUSION

NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Suicidality risk among adults with binge-eating disorder

OBJECTIVE

To estimate relative suicidality risk associated with binge-eating disorder (BED).

METHODS

Retrospective study of patients identified as having BED (N = 1042) and a matched general population cohort (N = 10,420) from the Optum electronic health record database between January 2009 and September 2015. Patients had ≥1 outpatient encounter with a provider who recognized BED during the 12-month baseline preceding entry date. Incidence and relative risk of suicidality were assessed.

RESULTS

Incidence per 1000 person-years (95% CI) of suicidal ideation and suicide attempts, respectively, was 31.1 (23.1, 41.0) and 12.7 (7.9, 19.4) in the BED cohort and 5.8 (4.7, 7.1) and 1.4 (0.9, 2.2) in the comparator cohort. Risk of suicidal ideation and suicide attempts was greater in the BED cohort (HR [95% CIs], 6.43 [4.42, 9.37]) than in the comparator cohort (HR [95% CI], 9.47 [4.99, 17.98]) during follow-up. After adjusting for psychiatric comorbidities, associations of suicidal ideation and suicide attempts with BED remained elevated in patients with BED having histories of suicidality.

CONCLUSIONS

Findings suggest that history of suicidality may result in an increased risk of suicidal ideation and suicide attempts in patients with BED relative to the general population. Psychiatric comorbidity burden may explain the elevated risk of these conditions in BED.

Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation

OBJECTIVE

We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.

MATERIALS AND METHODS

Using EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.

RESULTS

For the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.

DISCUSSION

Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.

CONCLUSIONS

Black or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.

Computer-aided therapeutic diagnosis for anorexia

Background

Anorexia nervosa is a clinical disorder syndrome of the wide spectrum without a fully recognized etiology. The necessary issue in the clinical diagnostic process is to detect the causes of this disease (e.g., my body image, food, family, peers), which the therapist gradually comes to by verifying assumptions using proper methods and tools for diagnostic process. When a person is diagnosed with anorexia, a clinician (a doctor, a therapist or a psychologist) proposes a therapeutic diagnosis and considers the kind of treatment that should be applied. This process is also continued during therapeutic diagnosis. In both cases, it is recommended to apply computer-aided tools designed for testing and confirming the assumptions made by a psychologist. The paper aims to present the computer-aided therapeutic diagnosis method for anorexia. The proposed method consists of 4 stages: free statements of a patient about his/her body image, the general sentiment analysis of statement based on Recurrent Neural Network, assessment of the intensity of five basic emotions: happiness, anger, sadness, fear and disgust (using the Nencki Affective Word List and conversion of words to their basic form), and the assessment of particular areas of difficulties—the sentiment analysis based on the dictionary approach was applied.

Results

The sentiment analysis of a document achieved 72% and 51% of effectiveness, respectively, for RNN and dictionary-based methods. The intensity of sadness (emotion) occurring within the dictionary method is differentiated between control and research group at the level of 10%.

Conclusion

The quick access to the sentiment analysis of a statement on the image of patient’s body, emotions experienced by the patient and particular areas of difficulties of people prone to the anorexia nervosa disorders, may help to establish the diagnosis in a very short time and start an immediate therapy. The proposed automatic method helps to avoid patient’s aversions towards the therapy, which may include avoiding patient–therapist communication, talking about less essential topics, coming late for the sessions. These circumstances can guarantee promising prognosis for recovering.

Association of depression and diabetes complications and mortality: a population-based cohort study

AIMS

Several studies suggested that depression might worsen the clinical outcome of diabetes mellitus; however, such association was confounded by duration of illness and baseline complications. This study aimed to assess whether depression increases the risk of diabetes complications and mortality among incident patients with diabetes.

METHODS

This was a population-based matched cohort study using Taiwan's National Health Insurance Research Database. A total of 38 537 incident patients with diabetes who had depressive disorders and 154 148 incident diabetes patients without depression who were matched by age, sex and cohort entry year were randomly selected. The study endpoint was the development of macrovascular and microvascular complications, all-cause mortality and cause-specific mortality.

RESULTS

Among participants, the mean (±SD) age was 52.61 (±12.45) years, and 39.63% were male. The average duration of follow-up for mortality was 5.5 years, ranging from 0 to 14 years. The adjusted hazard ratios were 1.35 (95% confidence interval [CI], 1.32-1.37) for macrovascular complications and 1.08 (95% CI, 1.04-1.12) for all-cause mortality. However, there was no association of depression with microvascular complications, mortality due to cardiovascular diseases or mortality due to diabetes mellitus. The effect of depression on diabetes complications and mortality was more prominent among young adults than among middle-aged and older adults.

CONCLUSIONS

Depression was associated with macrovascular complications and all-cause mortality in our patient cohort. However, the magnitude of association was less than that in previous studies. Further research should focus on the benefits and risks of treatment for depression on diabetes outcome.

A Review of Automatic Phenotyping Approaches using Electronic Health Records

Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort.

Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data

BACKGROUND

Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence.

OBJECTIVES

To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data.

METHODS

US Integrated Delivery Network data (2010-2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes-based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes-based and claims-based algorithms.

RESULTS

From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes-based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%-100%) for the EHR clinical notes-based algorithms and 94.6% (95% CI, 89.1%-97.8%) to 94.9% (95% CI, 89.8%-97.9%) for the claims-based algorithms.

CONCLUSIONS

The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence.

Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record

OBJECTIVE

Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR.

MATERIALS AND METHODS

We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations.

RESULTS

Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping.

DISCUSSION

These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics.

CONCLUSION

Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

Reducing the burden of suffering from eating disorders: Unmet treatment needs, cost of illness, and the quest for cost-effectiveness

Eating disorders are serious mental disorders as reflected in significant impairments in health and psychosocial functioning and excess mortality. Despite the clear evidence of clinical significance and despite availability of evidence-based, effective treatments, research has shown a paradox of elevated health services use and, yet, infrequent treatment specifically targeting the eating disorder (i.e., high unmet treatment need). This review paper summarizes key studies conducted in collaboration with G. Terence Wilson and offers an update of the research literature published since 2011 in three research areas that undergirded our collaborative research project: unmet treatment needs, cost of illness, and cost-effectiveness of treatments. In regards to unmet treatment needs, epidemiological studies find that the number of individuals with an eating disorder who do not receive disorder-specific treatment continues to remain high. Cost-of-illness show that eating disorders are associated with substantial financial burdens for individuals, their family, and society, yet comprehensive examination of costs across public sectors is lacking. Cost measures vary widely, making it difficult to draw firm conclusions. Hospitalization is a major driver of medical costs incurred by individuals with an eating disorder. Only a handful of cost-effectiveness studies have been conducted, leaving policy makers with little information on which to base decisions about allocation of resources to help reduce the burden of suffering attributable to eating disorders.

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

OBJECTIVE

To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP).

METHOD

A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers.

RESULTS

The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information.

CONCLUSIONS

The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.

Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes

Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity. In this study, we built a PAD surveillance system using natural language processing (NLP) for early detection of PAD from narrative clinical notes. Our NLP algorithm had excellent positive predictive value (0.93) and identified 41% of PAD cases before the initial ankle-brachial index (ABI) test date while in 12% of cases the NLP algorithm detected PAD on the same date as the ABI (the gold standard for comparison). Hence, our system ascertains PAD patients in a timely and accurate manner. In conclusion, our PAD surveillance NLP algorithm has the potential for translation to clinical practice for use in reminding clinicians to order ABI tests in patients with suspected PAD and to reinforce the implementation of guideline recommended risk modification strategies in patients diagnosed with PAD.

Characteristics and use of treatment modalities of patients with binge-eating disorder in the Department of Veterans Affairs

OBJECTIVE

In 2013 binge-eating disorder (BED) was recognized as a formal diagnosis, but was historically included under the diagnosis code for eating disorder not otherwise specified (EDNOS). This study compared the characteristics and use of treatment modalities in BED patients to those with EDNOS without BED (EDNOS-only) and to matched-patients with no eating disorders (NED).

METHODS

Patients were identified for this study from electronic health records in the Department of Veterans Affairs from 2000 to 2011. Patients with BED were identified using natural language processing and patients with EDNOS-only were identified by ICD-9 code (307.50). First diagnosis defined index date for these groups. NED patients were frequency matched to BED patients up to 4:1, as available, on age, sex, BMI, depression, and index month encounter. Baseline characteristics and use of treatment modalities during the post-index year were compared using t-tests or chi-square tests.

RESULTS

There were 593 BED, 1354 EDNOS-only, and 1895 matched-NED patients identified. Only 68 patients with BED had an EDNOS diagnosis. BED patients were younger (48.7 vs. 49.8years, p=0.04), more were male (72.2% vs. 62.8%, p<0.001) and obese (BMI 40.2 vs. 37.0, p<0.001) than EDNOS-only patients. In the follow-up period fewer BED (68.0%) than EDNOS-only patients (87.6%, p<0.001), but more BED than NED patients (51.9%, p<0.001) used at least one treatment modality.

DISCUSSION

The characteristics of BED patients were different from those with EDNOS-only and NED as was their use of treatment modalities. These differences highlight the need for a separate identifier of BED.

Healthcare costs and resource utilization of patients with binge-eating disorder and eating disorder not otherwise specified in the Department of Veterans Affairs

OBJECTIVE

The objective of this study was to compare the one-year healthcare costs and utilization of patients with binge-eating disorder (BED) to patients with eating disorder not otherwise specified without BED (EDNOS-only) and to matched patients without an eating disorder (NED).

METHODS

A natural language processing (NLP) algorithm identified adults with BED from clinical notes in the Department of Veterans Affairs (VA) electronic health record database from 2000 to 2011. Patients with EDNOS-only were identified using ICD-9 code (307.50) and those with NLP-identified BED were excluded. First diagnosis date defined the index date for both groups. Patients with NED were randomly matched 4:1, as available, to patients with BED on age, sex, BMI, depression diagnosis, and index month. Patients with cost data (2005-2011) were included. Total healthcare, inpatient, outpatient, and pharmacy costs were examined. Generalized linear models were used to compare total one-year healthcare costs while adjusting for baseline patient characteristics.

RESULTS

There were 257 BED, 743 EDNOS-only, and 823 matched NED patients identified. The mean (SD) total unadjusted one-year costs, in 2011 US dollars, were $33,716 ($38,928) for BED, $37,052 ($40,719) for EDNOS-only, and $19,548 ($35,780) for NED patients. When adjusting for patient characteristics, BED patients had one-year total healthcare costs $5,589 higher than EDNOS-only (p = 0.06) and $18,152 higher than matched NED patients (p < 0.001).

DISCUSSION

This study is the first to use NLP to identify BED patients and quantify their healthcare costs and utilization. Patients with BED had similar one-year total healthcare costs to EDNOS-only patients, but significantly higher costs than patients with NED.

Analysis of osteoporosis treatment patterns with bisphosphonates and outcomes among postmenopausal veterans

PURPOSE

Adherence and persistence with bisphosphonates are frequently poor, and stopping, restarting, or switching bisphosphonates is common. We evaluated bisphosphonate change behaviors (switching, discontinuing, or reinitiating) over time, as well as fractures and costs, among a large, national cohort of postmenopausal veterans.

METHODS

Female veterans aged 50+ treated with bisphosphonates during 2003-2011 were identified in Veterans Health Administration (VHA) datasets. Bisphosphonate change behaviors were characterized using pharmacy refill records. Patients' baseline disease severity was characterized based on age, T-score, and prior fracture. Cox Proportional Hazard analysis was used to evaluate characteristics associated with discontinuation and the relationship between change behaviors and fracture outcomes. Generalized estimating equations were used to evaluate the relationship between change behaviors and cost outcomes.

RESULTS

A total of 35,650 patients met eligibility criteria. Over 6800 patients (19.1%) were non-switchers. The remaining patients were in the change cohort; at least half displayed more than one change behavior over time. A strong, significant predictor of discontinuation was ≥5 healthcare visits in the prior year (11-23% more likely to discontinue), and discontinuation risk decreased with increasing age. No change behaviors were associated with increased fracture risk. Total costs were significantly higher in patients with change behaviors (4.7-19.7% higher). Change-behavior patients mostly had significantly lower osteoporosis-related costs than non-switchers (22%-118% lower).

CONCLUSIONS

Most bisphosphonate patients discontinue treatment at some point, which did not significantly increase the risk of fracture in this majority non-high risk population. Bisphosphonate change behaviors were associated with significantly lower osteoporosis costs, but significantly higher total costs.

High-frequency binge eating predicts weight gain among veterans receiving behavioral weight loss treatments

OBJECTIVE

To assess for the frequency of binge eating behavior and its association with weight loss in an overweight/obese sample of veterans.

METHODS

This study is a secondary analysis of data from the ASPIRE study, a randomized effectiveness trial of weight loss among veterans. Of the 481 enrolled veterans with overweight/obesity, binge eating frequency was obtained by survey for 392 (82%).

RESULTS

The majority (77.6%) reported binge eating, and 6.1% reported high-frequency binge eating. Those reporting any binge eating lost 1.4% of body weight, decreased waist circumference by 2.0 cm, and had significantly worse outcomes than those reporting never binge eating who lost about double the weight (2.7%) and reduced waist circumference by twice as much (4.2 cm). The high-frequency binge group gained 1.4% of body weight and increased waist circumference by 0.3 cm.

CONCLUSIONS

High rates of binge eating were observed in an overweight/obese sample of veterans enrolled in weight loss treatment. The presence of binge eating predicted poorer weight loss outcomes. Furthermore, high-frequency binge eating was associated with weight gain. These findings have operational and policy implications for developing effective strategies to address binge eating in the context of behavioral weight loss programs for veterans.