Clinician identification of youth abusing over-the-counter products for weight control in a large U.S. integrated health system

Background: Abuse of over-the-counter (OTC) products, such as diet pills and laxatives, for weight control by adolescents is well-documented and can precipitate serious medical conditions. Yet only a small percentage of youth with disordered weight control behaviors receive treatment. The objective of this study was to examine how often clinicians communicate with youth with symptoms consistent with abuse of OTC products for weight control about possible use of these products. We used electronic medical records and administrative claims for services for 53,229 12 to 17 year old patients receiving care from an integrated health system in the U.S. Northwest from August 2007 to December 2010. We examined electronic text of clinical notes to identify encounters in which the clinician noted one of 10 metabolic conditions potentially associated with abuse of OTC products (diet pills, laxatives, diuretics, ipecac, orlistat, and alli®) for weight control and then assessed whether clinicians noted communication with adolescent patients about possible use of OTC products for weight control. Results: We identified 130 (0.2% of sample) patients with clinical notes indicating one or more of the metabolic conditions. In clinical notes for only four (3.1%) of these patients did clinicians document suspicion or communication about possible abuse of the OTC products. All four had a previous eating disorder diagnosis. In the 12 months subsequent to the clinical encounter in which a metabolic disturbance was identified, medical notes for only three (2.3%) of the 130 patients indicated clinician suspicion or communication about possible abuse of these products or an eating disorder. Conclusions: Clinicians are missing a critical window of opportunity to query adolescents when presenting with suspicious metabolic disturbances about possible abuse of OTC products for weight control. Clinicians may need more training to detect OTC product abuse, and electronic medical records should prompt more thorough enquiry

Patterns of initiation of second generation antipsychotics for bipolar disorder: a month-by-month analysis of provider behavior

Background: Several second generation antipsychotics (SGAs) received FDA approval for bipolar disorder in the 2000s. Although efficacious, they have been costly and may cause significant side effects. Little is known about the factors associated with prescribers’ decisions to initiate SGA prescriptions for this condition. Methods: We gathered administrative data from the Department of Veterans Affairs on 170,713 patients with bipolar disorder between fiscal years 2003–2010. Patients without a prior history of taking SGAs were considered eligible for SGA initiation during the study (n =126,556). Generalized estimating equations identified demographic, clinical, and comorbidity variables associated with initiation of an SGA prescription on a month-by-month basis. Results: While the number of patients with bipolar disorder using SGAs nearly doubled between 2003 and 2010, analyses controlling for patient characteristics and the rise in the bipolar population revealed a 1.2% annual decline in SGA initiation during this period. Most medical comorbidities were only modestly associated with overall SGA initiation, although significant differences emerged among individual SGAs. Several markers of patient severity predicted SGA initiation, including previous hospitalizations, psychotic features, and a history of other antimanic prescriptions; these severity markers became less firmly linked to SGA initiation over time. Providers in the South were somewhat more likely to initiate SGA treatment. Conclusions: The number of veterans with bipolar disorder prescribed SGAs is rising steadily, but this increase appears primarily driven by a corresponding increase in the bipolar population. Month-by-month analyses revealed that higher illness severity predicted SGA initiation, but that this association may be weakening over time

Geographic disparities in the risk of perforated appendicitis among children in Ohio: 2001–2003

<p>Abstract</p> <p>Background</p> <p>Rural-urban disparities in health and healthcare are often attributed to differences in geographic access to care and health seeking behavior. Less is known about the differences between rural locations in health care seeking and outcomes. This study examines how commuting patterns in different rural areas are associated with perforated appendicitis.</p> <p>Results</p> <p>Controlling for age, sex, insurance type, comorbid conditions, socioeconomic status, appendectomy rates, hospital type, and hospital location, we found that patient residence in a rural ZIP code with significant levels of commuting to metropolitan areas was associated with higher risk of perforation compared to residence in rural areas with commuting to smaller urban clusters. The former group was more likely to seek care in an urbanized area, and was more likely to receive care in a Children's Hospital.</p> <p>Conclusion</p> <p>To our knowledge, this is the first study to differentiate rural dwellers with respect to outcomes associated with appendicitis as opposed to simply comparing "rural" to "urban". Risk of perforated appendicitis associated with commuting patterns is larger than that posed by several individual indicators including some age-sex cohort effects. Future studies linking the activity spaces of rural dwellers to individual patterns of seeking care will further our understanding of perforated appendicitis and ambulatory care sensitive conditions in general.</p

Effect of Pediatric Behavioral Health Screening and Colocated Services on Ambulatory and Inpatient Utilization

Objective The study sought to determine the impact of a pediatric behavioral health (BH) screening and co-location model on BH care utilization Methods In 2003, Cambridge Health Alliance, a Massachusetts public health system introduced BH screening and co-location of social workers within its pediatric practices in a sequential manner. An interrupted time series study of the change in trends of ambulatory, emergency and inpatient BH utilization in the 30 months following model implementation compared to the 18 months prior was conducted to determine the impact of this model on BH care utilization, Utilization data on 11,223 children ≥4 years 9 months to < 18 years 3 months seen from 2003 to 2008, contributed to the study. Results In the 30 months following implementation of pediatric BH screening and co-location there was a 20.4% cumulative increase in specialty BH visit rates (trend = 0.013% per month; p=0.049), and 67.7% cumulative increase in BH primary care visit rates (trend = 0.019% per month; p=0.002) compared to the expected rate predicted by the 18 month pre- intervention trend. In addition, BH emergency department visit rates increased 245% compared to the expected rate (trend = 0.01% per month; p<.001). Conclusions Following the implementation of a BH screening/co-location model, more children received BH treatment. Contrary to expectations, BH emergency department visits also increased. Further study is needed to determine if this is an effect of how care was organized for children newly engaged in BH care or a reflection of secular trends in BH utilization or both

Predictors of Serious Complications Associated with Lower Gastrointestinal Endoscopy in a Major City-Wide Health Region

BACKGROUND: There are limited data regarding complications associated with colonoscopy and flexible sigmoidoscopy in usual clinical practice in Canada

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods

Supplementary information files for "Acceptability of linking individual credit, financial and public records data to healthcare records for suicide risk machine learning models"

Supplementary information files for article "Acceptability of linking individual credit, financial and public records data to healthcare records for suicide risk machine learning models"BACKGROUNDIndividual-level information about negative life events (NLE) such as debt burden, bankruptcy, foreclosure, divorce, and criminal arrest might improve the accuracy of machine learning models used by health systems for suicide risk prediction. Information about NLEs is routinely collected by credit bureaus and other vendors and can be linked to individual-level healthcare records. However, little is known about the acceptability of linking NLE data collected outside healthcare.OBJECTIVES/METHODSTo assess preferences for linking external NLE data to healthcare records for suicide prevention. We conducted a discrete choice experiment (DCE) among Kaiser Permanente Washington (KPWA) members comparing different suicide risk prediction programs. Preferences were estimated using conditional logistic regression and latent class analysis.RESULTSThere were 743 participants. Naïve to any protections for privacy or autonomy, participant willingness to link data varied by the type of information to be linked, demographic characteristics, and experience with negative life events. Results of the DCE modeling indicated that overall, 65.1% of people were willing to link data and 34.9% were more private. Trust in KPWA to safeguard data was the strongest predictor of willingness to link data.CONCLUSIONSA majority of participants were willing to have their credit bureau data linked to healthcare records for suicide prediction and prevention.LAY SUMMARYInformation about life events such as bankruptcy, foreclosure and divorce might improve our ability to predict who is at risk of making a suicide attempt. Such information is routinely collected by credit bureaus and could be linked to healthcare records. But, little is known about whether people find this data linkage acceptable. This study asked people to choose which data management strategies they prefer. Of the 7720 people asked to complete the survey, 743 people responded. Preferences varied by demographic characteristics such as age, race, and experience with negative life events. Overall, about 65% of people reported that they would be willing to have their data linked provided certain safeguards were in place. The most important factor in a person being willing to have their data linked was how much they trust Kaiser Permanente to protect their information.©The Authors, CC BY 4.0</p

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation

Changes in antidepressant use by young people and suicidal behavior after FDA warnings and media coverage: quasi-experimental study

Objective To investigate if the widely publicized warnings in 2003 from the US Food and Drug Administration about a possible increased risk of suicidality with antidepressant use in young people were associated with changes in antidepressant use, suicide attempts, and completed suicides among young people. Design Quasi-experimental study assessing changes in outcomes after the warnings, controlling for pre-existing trends. Setting Automated healthcare claims data (2000-10) derived from the virtual data warehouse of 11 health plans in the US Mental Health Research Network. Participants Study cohorts included adolescents (around 1.1 million), young adults (around 1.4 million), and adults (around 5 million). Main outcome measures Rates of antidepressant dispensings, psychotropic drug poisonings (a validated proxy for suicide attempts), and completed suicides. Results Trends in antidepressant use and poisonings changed abruptly after the warnings. In the second year after the warnings, relative changes in antidepressant use were −31.0% (95% confidence interval −33.0% to −29.0%) among adolescents, −24.3% (−25.4% to −23.2%) among young adults, and −14.5% (−16.0% to −12.9%) among adults. These reflected absolute reductions of 696, 1216, and 1621 dispensings per 100 000 people among adolescents, young adults, and adults, respectively. Simultaneously, there were significant, relative increases in psychotropic drug poisonings in adolescents (21.7%, 95% confidence interval 4.9% to 38.5%) and young adults (33.7%, 26.9% to 40.4%) but not among adults (5.2%, −6.5% to 16.9%). These reflected absolute increases of 2 and 4 poisonings per 100 000 people among adolescents and young adults, respectively (approximately 77 additional poisonings in our cohort of 2.5 million young people). Completed suicides did not change for any age group. Conclusions Safety warnings about antidepressants and widespread media coverage decreased antidepressant use, and there were simultaneous increases in suicide attempts among young people. It is essential to monitor and reduce possible unintended consequences of FDA warnings and media reporting

A literature review of the disruptive effects of user fee exemption policies on health systems

BACKGROUND: Several low- and middle-income countries have exempted patients from user fees in certain categories of population or of services. These exemptions are very effective in lifting part of the financial barrier to access to services, but they have been organized within unstable health systems where there are sometimes numerous dysfunctions. The objective of this article is to bring to light the disruptions triggered by exemption policies in health systems of low- and middle-income countries. METHODS: Scoping review of 23 scientific articles. The data were synthesized according to the six essential functions of health systems. RESULTS: The disruptions included specifically: 1) immediate and significant increases in service utilization; 2) perceived heavier workloads for health workers, feelings of being exploited and overworked, and decline in morale; 3) lack of information about free services provided and their reimbursement; 4) unavailability of drugs and delays in the distribution of consumables; 5) unpredictable and insufficient funding, revenue losses for health centres, reimbursement delays; 6) the multiplicity of actors and the difficulty of identifying who is responsible ('no blame' game), and deficiencies in planning and communication. CONCLUSIONS: These disruptive elements give us an idea of what is to be expected if exemption policies do not put in place all the required conditions in terms of preparation, planning and complementary measures. There is a lack of knowledge on the effects of exemptions on all the functions of health systems because so few studies have been carried out from this perspective