Persistent Disparities: Trends in Rates of Sheltered Homelessness Across Demographic Subgroups in the USA.

CONTEXT: Homelessness is a public health crisis affecting millions of Americans every year, with severe consequences for health ranging from infectious diseases to adverse behavioral health outcomes to significantly higher all-cause mortality. A primary constraint of addressing homelessness is a lack of effective and comprehensive data on rates of homelessness and who experiences homelessness. While other types of health services research and policy are based around comprehensive health datasets to successfully evaluate outcomes and link individuals with services and policies, there are few such datasets that report homelessness. METHODS: Gathering archived data from the US Department of Housing and Urban Development, we created a unique dataset of annual rates of homelessness, nationally, as measured by persons accessing homeless shelter systems, for 11 years (2007-2017, including the Great Recession and prior to the start of the 2020 pandemic). Responding to the need to measure and address racial and ethnic disparities in homelessness, the dataset reports annual rates of homelessness across HUD selected, Census-based racial and ethnic categories. FINDINGS: Between 2007 and 2017, across all types of sheltered homelessness, whether individual, family, or total, Black, American Indian or Alaska Native, and Native Hawaiian and Pacific Islander individuals and families were far more likely to experience homelessness than non-Hispanic White individuals and families. Particularly concerning about the rates of homelessness among these populations is the persistent and increasing nature of these disparities across the entire study period. CONCLUSIONS: While homelessness is a public health problem, the hazard of experiencing homelessness is not uniformly distributed across different populations. Because homelessness is such a strong social determinant of health and risk factor across multiple health domains, it deserves the same careful annual tracking and evaluation by public health stakeholders as other areas of health and health care.

Examining Racial/Ethnic Disparities in Tobacco Dependence Treatment Among Medicaid Beneficiaries Using Fifty State Medicaid Claims, 2009-2014.

In the USA, low-income racial/ethnic minority groups experience higher smoking rates and greater smoking-related disease burden than their White counterparts. Despite the adverse effects, racial/ethnic minorities are less likely to access tobacco dependence treatment (TDT). Medicaid is one of the largest payers of TDT in the USA and covers predominantly low-income populations. The extent of TDT use among beneficiaries from distinct racial/ethnic groups is unknown. The objective is to estimate racial/ethnic differences in TDT use among Medicaid fee-for-service beneficiaries. Using a retrospective study design and 50 state (including the District of Columbia) Medicaid claims (2009-2014), we employed multivariable logistic regression models and predictive margin methods to estimate TDT use rates among adults (18-64) enrolled (≥ 11 months) in Medicaid fee-for-service programs (January 2009-December 2014) by race/ethnicity. The population included White (n = 6,536,004), Black (n = 3,352,983), Latinx (n = 2,264,647), Asian (n = 451,448), and Native American/Alaskan Native (n = 206,472) beneficiaries. Dichotomous outcomes reflected service use in the past year. Any TDT use was operationalized as any smoking cessation medication fill, any smoking cessation counseling visit, or any smoking cessation outpatient visit. In secondary analyses, we disaggregated TDT use into three separate outcomes. Results suggested that Black (10.6%; 95% CI = 9.9-11.4%), Latinx (9.5%; 95% CI = 8.9-10.2%), Asian (3.7%; 95% CI = 3.4-4.1%), and Native American/Alaskan Native (13.7%; 95% CI = 12.7-14.7%) beneficiaries had lower TDT use rates compared to White beneficiaries (20.6%). Similar racial/ethnic treatment disparities were identified across all outcomes. By identifying significant racial/ethnic disparities in TDT use between 2009 and 2014, this study provides a benchmark against which to measure recent interventions in state Medicaid programs improving equity in smoking cessation interventions.

Predictors of relapse and engagement in care one year after ending services in an urban safety net coordinated specialty care program for first episode psychosis.

OBJECTIVE: This study aimed to identify risk factors for relapse (psychiatric emergency department visits or hospitalization) and lack of follow-up with outpatient psychiatric care in the 12 months after ending services in an urban safety net coordinated specialty care (CSC) program for first episode psychosis (FEP). METHODS: The study population (n = 143) were individuals with FEP who had any CSC care between 2014 and 2021. To identify risk factors for relapse and follow up after exit, multivariable logistic regression was performed using data from electronic health records and linked insurance claims data. RESULTS: Individuals with any emergency department visit or hospitalization 12 months prior to ending CSC (aOR = 4.69, 95 % CI 1.78-12.34) and those who were using cannabis at last CSC contact (aOR = 4.06, 95 % CI 1.56-10.56) had a higher risk of relapse after ending CSC services. Cannabis use at last contact was also associated with lower rates of outpatient psychiatric follow-up (aOR = 0.32, 95 % CI 0.12-0.94), while CSC duration in months had a small positive association with post-CSC psychiatric follow-up. There were no differences in relapse or follow-up by race or ethnicity, primary diagnosis, or medication usage. CONCLUSIONS: Prior relapse during CSC predicted relapse in the 12 months after ending CSC services, but not outpatient follow up. Cannabis use predicted both a higher rate of relapse and a lower rate of follow up after ending services. There were no differences by race or ethnicity in our sample, suggesting that once individuals engaged in FEP care there were no evident disparities in the observed outcomes.

Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.

BACKGROUND: The objective was to develop and assess performance of an algorithm predicting suicide-related ICD codes within three months of psychiatric discharge. METHODS: This prognostic study used a retrospective cohort of EHR data from 2789 youth (12 to 20 years old) hospitalized in a safety net institution in the Northeastern United States. The dataset combined structured data with unstructured data obtained through natural language processing of clinical notes. Machine learning approaches compared gradient boosting to random forest analyses. RESULTS: Area under the ROC and precision-recall curve were 0.88 and 0.17, respectively, for the final Gradient Boosting model. The cutoff point of the model-generated predicted probabilities of suicide that optimally classified the individual as high risk or not was 0.009. When applying the chosen cutoff (0.009) to the hold-out testing set, the model correctly identified 8 positive cases out of 10, and 418 negative cases out 548. The corresponding performance metrics showed 80 % sensitivity, 76 % specificity, 6 % PPV, 99 % NPV, F-1 score of 0.11, and an accuracy of 76 %. LIMITATIONS: The data in this study comes from a single health system, possibly introducing bias in the model's algorithm. Thus, the model may have underestimated the incidence of suicidal behavior in the study population. Further research should include multiple system EHRs. CONCLUSIONS: These performance metrics suggest a benefit to including both unstructured and structured data in design of predictive algorithms for suicidal behavior, which can be integrated into psychiatric services to help assess risk.

Intensive Care Unit Bypass for Robotic-Assisted Single-Vessel Coronary Artery Bypass Grafting.

BACKGROUND: Fast-track and enhanced recovery after cardiac surgical procedures have shown reductions in intensive care unit (ICU) and hospital lengths of stay, with unchanged outcomes. However, cost reduction by an ultra-fast-track protocol after minimally invasive cardiac operations, without compromising clinical benefits, has yet to be demonstrated. METHODS: A total of 215 consecutive patients underwent robotic-assisted coronary artery bypass grafting, with 156 preoperatively stratified into conventional ICU recovery vs 59 candidates for a defined ICU-bypass protocol involving recovery room and floor care. Of these, 40 candidates completed the protocol, and 19 had conversion-to-ICU recovery. Because of right-skewed distribution, inpatient cost was log-transformed, and linear regression models were constructed to estimate geometric mean ratios (GMRs) comparing inpatient cost for these groups (conventional ICU recovery, ICU-bypass, conversion-to-ICU recovery), adjusted for The Society of Thoracic Surgeons Predicted Risk of Mortality score. RESULTS: Compared with the conventional ICU group, the ICU-bypass group conferred a 15% reduction in total inpatient (GMR, 0.85; P = .0007) and a 14% reduction in total variable direct costs (GMR, 0.86; P = .003). Compared with the conventional ICU group, the ICU-bypass and conversion-to-ICU groups had similar net hospital stay reductions (1.6-1.7 days). Relative to the conventional ICU group, ICU and floor duration were shortened after conversion to ICU, with a trend to reduced costs. Cardiac arrest, 30-day mortality, and stroke were absent, and other key adverse events did not differ between groups. CONCLUSIONS: A selective, successful ultra-fast-track ICU-bypass protocol for robotic-assisted coronary artery bypass grafting reduces inpatient cost without affecting short-term outcomes. Conversion-to-ICU recovery also maintains outcomes and trends toward reduced costs.

Effects of Medicaid coverage on receipt of tobacco dependence treatment among Medicaid beneficiaries with substance use disorder.

OBJECTIVE: Individuals with substance use disorder (SUD) smoke cigarettes at a rate that is more than double the rate of the general population. Tobacco dependence treatment (TDT) is effective at reducing smoking, yet it is unclear whether expanding insurance coverage of these services increases TDT use among Medicaid beneficiaries with SUD. DATA SOURCE: 2009-2013 Medicaid data in all 50 states and Washington DC. STUDY DESIGN: We conducted a retrospective analysis of the 2009-2013 de-identified Medicaid Analytic Extract (MAX) claims for a 100% national sample of fee-for-service (FFS) Medicaid adult beneficiaries. Using a difference-in-difference-in-differences analysis, we assessed the association of full TDT coverage on TDT medication use and tobacco cessation counseling services between beneficiaries with and without SUD. We adjusted for age, sex, race/ethnicity, diagnosis of co-occurring chronic illness, state tobacco taxes, and state and year fixed effects. DATA COLLECTION/EXTRACTION METHODS: We excluded patients not continuously enrolled in Medicaid for 12 months during the calendar year, adults aged 65 and older (given their dual enrollment in Medicaid and Medicare), minors aged 12-17, and pregnant women (for whom different TDT coverage policies apply). PRINCIPAL FINDINGS: We separately modeled the association between full coverage of (1) counseling, (2) over-the-counter nicotine replacement therapy, and (3) prescription cessation medications on TDT medication treatment and counseling services. We found that each coverage led to increases in any TDT medication treatment and counseling services for beneficiaries with SUD. The effects of each coverage on medication treatment were greater for beneficiaries with SUD compared to beneficiaries without SUD (ranging from 4.9 to 6.1 percentage point difference). CONCLUSION: Coverage of tobacco cessation counseling, over-the-counter nicotine replacement therapy, and prescription cessation medications holds promise for reducing the wide disparities in rates of smoking between those with and without SUD.

Assessing the Long-Term Effectiveness of a Behavioral Health Home for Adults With Bipolar and Psychotic Disorders.

OBJECTIVE: This study aimed to examine the impact of a behavioral health home (BHH) to better understand its potential to improve health for individuals with serious mental illness. METHODS: Propensity score-weighted interrupted time series analysis was used to estimate service utilization and chronic disease management through 3.5 years after BHH implementation and to compre BHH enrollees (N=413) with other patients with serious mental illness in the same health system (N=1,929). RESULTS: Relative to control group members, BHH patients had an immediate increase in primary care visits (+0.18 visits/month), which remained higher throughout follow-up, and an immediate decrease in emergency department visits (-0.031 visits/month). Behavioral health outpatient visits, which were increasing for BHH participants before implementation, began decreasing postimplementation; this decrease (-0.016 visits/month) was significantly larger than for the control group. Inpatient and outpatient visits for general medical health were decreasing over time for both groups before implementation but decreased more slowly for BHH patients postimplementation. Although behavioral health inpatient visits decreased for both groups around the start of the BHH program and remained lower, this initial drop was larger for the non-BHH group. BHH participation was associated with decreases in hemoglobin A1c values but no shift in low-density lipoprotein cholesterol values. CONCLUSIONS: The results reflect the challenges of improving health for patients with serious mental illness, even as access to primary care is increased. Further study is needed about which complex interventions inside and outside of the health care system can help offset the 20- to 30-year mortality gap faced by this population.

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

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

Disparities in Suicidality by Gender Identity Among Medicare Beneficiaries.

INTRODUCTION: Suicidality is higher for gender minorities than the general population, yet little is known about suicidality in disabled or older adult gender minorities. METHODS: This study used 2009-2014 Medicare claims to identify people with gender identity-related diagnosis codes (disabled, n=6,678; older adult, n=2,018) and compared their prevalence of suicidality with a 5% random non-gender minority beneficiary sample (disabled, n=535,801; older adult, n=1,700,008). Correlates of suicidality were assessed (via chi-square) for each of the 4 participant groups separately, and then disparities within eligibility status (disabled or older adult) were assessed using logistic regression models, adjusting first for age and mental health chronic conditions and then additionally for Medicaid eligibility, race/ethnicity, or U.S. region (each separately). The primary hypotheses were that gender minority beneficiaries would have higher suicidality but that suicidality disparities would persist after adjusting for covariates. Data were analyzed between 2017 and 2019. RESULTS: Gender minority beneficiaries had higher unadjusted suicidality than non-gender minority beneficiaries in the disabled cohort (18.5% vs 7.1%, p<0.001). Significant suicidality predictors in all 4 groups included the following: age (except in older adult gender minorities), Medicaid eligibility, depression or behavioral health conditions, avoidable hospitalizations, and violence victimization. In age- and mental health-adjusted logistic regression models, gender minorities had higher odds of suicidality than non-gender minority beneficiaries (disabled, OR=1.95, p<0.0001; older adult, OR=2.10, p<0.0001). Disparities were not attenuated after adjusting for Medicaid eligibility, race/ethnicity, or region. CONCLUSIONS: Heightened suicidality among identified gender minority Medicare beneficiaries highlights a pressing need to identify and reduce barriers to wellness in this population.

High Prevalence of the Copepod Salmincola californiensis in Steelhead Trout in Lake Ontario Following its Recent Invasion.

Salmincola californiensis (Dana, 1853) (Subclass Copepoda: Family Lernaeopodidae) is known to parasitize salmonids of the genus Oncorhynchus including Oncorhynchus mykiss (rainbow trout), Oncorhynchus tshawytscha (chinook salmon), and Oncorhynchus kisutch (coho salmon). These 3 salmonids have been introduced to the Great Lakes intermittently since the mid-1800s. As we demonstrate here, the introduction of these salmonids to the Great Lakes was followed, at some point, by the introduction of their parasitic gill copepod, S. californiensis. Given anecdotal accounts of S. californiensis in introduced salmonids in Lake Ontario since 2012, we chose to conduct a survey to formally document the occurrence of this introduced species. Our survey took place during spring, summer, and fall of 2018 and during spring of 2019 at the south-eastern side of Lake Ontario. Prevalence of S. californiensis was 69, with a mean intensity of 2.7 in 61 rainbow trout examined in 2018. In 2019, prevalence of S. californiensis was 71, with a mean intensity of 3.6 in 59 rainbow trout examined. The prevalence of S. californiensis was 39, with a mean intensity of 1.6 in 223 chinook salmon examined in 2018. No specimens of S. californiensis were found in the 100 coho salmon examined in 2018. The prevalence of S. californiensis in rainbow trout is of great concern considering that it is double that found in rainbow trout in the native range (69 [in 2018] and 71 [in 2019] vs. 35). This is the first formal documentation of the invasion of S. californiensis in Lake Ontario. Future fisheries management decisions in Lake Ontario and its tributaries should take into account these data.

Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data.

AIM: To identify associations between opioid-related mortality and neighborhood-level risk factors. DESIGN: Cross-sectional study. SETTING: Massachusetts, USA. PARTICIPANTS: Using 2011-14 Massachusetts death certificate data, we identified opioid-related (n = 3089) and non-opioid-related premature deaths (n = 8729). MEASUREMENTS: The independent variables consisted of four sets of neighborhood-level factors: (1) psychosocial, (2) economic, (3) built environment and (4) health-related. At the individual level we included the following compositional factors: age at death, sex, race/ethnicity, marital status, education, veteran status and nativity. The primary outcome of interest was opioid-related mortality. FINDINGS: Multi-level models identified number of social associations per 10 000 [odds ratio (OR) = 0.84, P = 0.002, 95% confidence interval (CI) = 0.75-0.94] and number of hospital beds per 10 000 (OR = 0.78, P < 0.001, 95% CI = 0.68-0.88) to be inversely associated with opioid-related mortality, whereas the percentage living in poverty (OR = 1.01, P = 0.008, 95% CI = 1.00-1.01), food insecurity rate (OR = 1.21, P = 0.002, 95% CI = 1.07-1.37), number of federally qualified health centers (OR = 1.02, P = 0.028, 95% CI = 1.02-1.08) and per-capita morphine milligram equivalents of hydromorphone (OR = 1.05, P = 0.003, 95% CI = 1.01-1.08) were positively associated with opioid-related mortality. CONCLUSIONS: Opioid-related deaths between 2011 and 2014 in the state of Massachusetts appear to be positively associated with the percentage living in poverty, food insecurity rate, number of federally qualified health centers and per-capita morphine milligram equivalents of hydromorphone, but inversely associated with number of social associations per 10 000 and number of hospital beds per 10 000.

A Comparison of Health Care Expenditures for Medicaid-Insured Children with Autism Spectrum Disorder and Asthma in an Expanding Accountable Care Organization.

As value-based care continues to expand, more children with autism spectrum disorder (ASD) will be treated by accountable care organizations (ACOs), provider organizations seeking to improve population health while reducing costs. To inform ACO strategies for children with ASD, this study compared health care expenditures of children insured by a Medicaid managed care organization, empaneled to a safety net ACO, with ASD, asthma, and neither diagnosis. Compared to other study groups, children with ASD were more costly, had lower rates of acute care, and had higher rates of "leaked" care provided by home- and community-based mental health agencies outside of the ACO. These findings highlight the need for unique value-based strategies for children with ASD in a public sector ACO.

Trends in Mental Health Care Use in Medicare from 2009 to 2014 by Gender Minority and Disability Status.

Purpose: This study examines trends in Medicare beneficiaries' mental health care use from 2009 to 2014 by gender minority and disability status. Methods: Using 2009 to 2014 Medicare claims, we modeled mental health care use (outpatient mental health care, inpatient mental health care, and psychotropic drugs) over time, adjusting for age and behavioral health diagnoses. We compared trends for gender minority beneficiaries (identified using diagnosis codes) to trends for a 5% random sample of other beneficiaries, stratified by original entitlement reason (age vs. disability). Results: Adjusted outpatient and inpatient mental health care use decreased and differences generally narrowed between gender minority and other beneficiaries over the study period. Among beneficiaries qualifying through disability, the gap in the number of outpatient and inpatient visits (among those with at least one visit in a given year) widened. Psychotropic drug use rose for all beneficiaries, but the proportion of gender minority beneficiaries in the aged cohort who had a psychotropic medication prescription rose faster than for other aged beneficiaries. Conclusions: Mental health care needs for Medicare beneficiaries may be met increasingly by using psychotropic medications rather than outpatient visits, and this pattern is more pronounced for identified gender minority (especially aged) beneficiaries. These trends may indicate a growing need for research and provider training in safe and effective psychotropic medication prescribing alongside gender-affirming treatments such as hormone therapy, especially for aged gender minority individuals who likely already experience polypharmacy.

Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records.

OBJECTIVE: The rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity to address the pressing public health problem of adolescent suicidal behavior. We describe the development and evaluation of a machine learning algorithm using natural language processing of electronic health records to identify suicidal behavior among psychiatrically hospitalized adolescents. METHODS: Adolescents hospitalized on a psychiatric inpatient unit in a community health system in the northeastern United States were surveyed for history of suicide attempt in the past 12 months. A total of 73 respondents had electronic health records available prior to the index psychiatric admission. Unstructured clinical notes were downloaded from the year preceding the index inpatient admission. Natural language processing identified phrases from the notes associated with the suicide attempt outcome. We enriched this group of phrases with a clinically focused list of terms representing known risk and protective factors for suicide attempt in adolescents. We then applied the random forest machine learning algorithm to develop a classification model. The model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: The final model had a sensitivity of 0.83, specificity of 0.22, AUC of 0.68, a PPV of 0.42, NPV of 0.67, and an accuracy of 0.47. The terms mostly highly associated with suicide attempt clustered around terms related to suicide, family members, psychiatric disorders, and psychotropic medications. CONCLUSION: This analysis demonstrates modest success of a natural language processing and machine learning approach to identifying suicide attempt among a small sample of hospitalized adolescents in a psychiatric setting.

Identifying Gender Minority Patients' Health And Health Care Needs In Administrative Claims Data.

Health care utilization patterns for gender minority Medicare beneficiaries (those who are transgender or gender nonbinary people) are largely unknown. We identified gender minority beneficiaries using a diagnosis-code algorithm and compared them to a 5 percent random sample of non-gender minority beneficiaries from the period 2009-14 in terms of mental health and chronic diseases, use of preventive and mental health care, hospitalizations, and emergency department (ED) visits. Gender minority beneficiaries experienced more disability and mental illness. When we adjusted for age and mental health, we found that they used more mental health care. And when we adjusted for age and chronic conditions, we found that they were more likely to be hospitalized and to visit the ED. There were several small but significant differences in preventive care use. Findings were similar for disabled and older cohorts. These findings underscore the need to capture gender identity in health data to better address this population's health needs.

Mind the Gap: Developing an Integrated Behavioral Health Home to Address Health Disparities in Serious Mental Illness.

OBJECTIVE: This study evaluated the impact of an integrated behavioral health home (BHH) pilot on adults with psychotic and bipolar disorders. METHODS: Quasi-experimental methods were used to compare outcomes before (September 2014-August 2015) and after the intervention (September 2015-August 2016) among ambulatory BHH patients and a control group. Electronic health records were compared between 424 BHH patients (N=369, psychotic disorder; N=55, bipolar disorder) and 1,521 individuals from the same urban, safety-net health system who were not enrolled in the BHH. Groups were weighted by propensity score on the basis of sex, age, race-ethnicity, language, 2010 U.S. Census block group characteristics, Medicare and Medicaid enrollment, and diabetes diagnosis. RESULTS: BHH patients had fewer total psychiatric hospitalizations and fewer total emergency visits compared with the control group, a difference that was predominantly driven by patients with at least one psychiatric hospitalization or ED visit. There were no differences in medical hospitalizations. Although BHH patients were more likely to receive HbA1c screening, there were no differences between the groups in lipid monitoring. Regarding secondary outcomes, there were no significant differences in changes in metabolic monitoring parameters among patients with diabetes. CONCLUSIONS: Participation in a pilot ambulatory BHH program among patients with psychotic and bipolar disorders was associated with significant reductions in ED visits and psychiatric hospitalizations and increased HbA1c monitoring. This evaluation builds on prior research by specifying intervention details and the clinical target population, strengthening the evidence base for care integration to support further program dissemination.

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, "how do you feel today?" We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.

Pursuing cost-effectiveness in mental health service delivery for youth with complex needs.

BACKGROUND: Mental health advocates seek to expand children's services, noting widespread failure to meet the needs of public sector youth suffering from serious emotional disturbance (SED). However, state and national budgets face deepening cuts, with rising health care costs taking the blame. As the gap between needs and finances widens, identification of cost-effective treatments that will benefit children with SED and their families is of increasing importance. Community-based interventions for this population, such as the wraparound approach and systems-of-care, are being disseminated but literature is scant regarding effects on expense. The Mental Health Services Program for Youth (MHSPY) model is aligned philosophically with wraparound and systems-of-care but unique in blending public agency dollars to deliver integrated medical, mental health and social services. MHSPY's linked clinical and expense data is useful to study community-based treatment cost-effectiveness. AIMS OF STUDY: To examine the cost-effectiveness of an intensively integrated, family and community-based clinical intervention for youth with mental health needs in comparison to "usual care.'' METHODS: Study and reference populations were matched on age, gender, community, psychiatric diagnosis, morbidity and insurance type. Claims analyses included patterns of service utilization and medical expense for both groups. Using propensity score matching, results for study youth are compared with results for the population receiving "usual care.'' Clinical functioning was measured for the intervention group at baseline and 12 months. RESULTS: The intervention group used lower intensity services and had substantially lower claims expense (e.g. 32% lower for emergency room, 74% lower for inpatient psychiatry) than their matched counterparts in the "usual care'' group. Intervention youth were consistently maintained in least restrictive settings, with over 88% of days spent at home and showed improved clinical functioning on standard measures. DISCUSSION: The intensive MHSPY model of service delivery offers potential as a cost-effective intervention for complex youth. Its integrated approach, recognizing needs across multiple life domains, appears to enhance engagement and the effectiveness of mental health treatment, resulting in statistically significant clinical improvements. Functional measures are not collected in "usual care,'' limiting comparisons. However, claims expense for intervention youth was substantially lower than claims expense for Medicaid comparison youth, suggesting clinical needs for intervention youth post-enrollment were lower than for those receiving "usual care.'' IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The MHSPY model, which intentionally engages families in "clustered'' traditional and non-traditional services, represents a replicable strategy for enhancing the impact of clinical interventions, thereby reducing medical expense. IMPLICATIONS FOR HEALTH POLICIES: Blending categorical state agency dollars and insurance funds creates flexibility to support community-based care, including individualized services for high-risk youth. Resulting expenses total no more, and are often less, than "treatment as usual'' but yield greater clinical benefits. IMPLICATIONS FOR FURTHER RESEARCH: Further research is needed regarding which intervention elements contribute the most towards improved clinical functioning, as well as which patients are most likely to benefit. A randomized trial of MHSPY vs. "usual care,'' including examination of the sustainability of effects post-disenrollment, would provide a chance to further test this innovative model.

Sharps management in hospital: an audit of equipment, practice and awareness.

The safe handling and disposal of needles and other sharp instruments forms part of an overall strategy to protect staff, patients and visitors from exposure to blood-borne pathogens. As with many infection prevention and control policies, the assessment and management of the risks associated with the use of sharps is paramount, and safe systems of work and engineering controls must be in place to minimize any identified risks. The use of sharps in hospitals should be avoided where possible; when their use is essential, particular care is required in handling and disposal - if possible, use safer sharps devices. An audit of sharps management was undertaken to observe equipment, practice and awareness. The audit reported very positive results. However, some areas needed further review to improve practice. The infection control team implemented an action plan as a result of the audit and set about initiating measures for training and awareness. It is necessary to audit sharps management routinely to have an accurate assessment of current practice and prevent occupational exposure to blood-borne pathogens.

MHSPY: a children's health initiative for maintaining at-risk youth in the community.

The Massachusetts Mental Health Services Program for Youth (MHSPY) is a home-based clinical intervention that seeks to maintain youth with severe functional impairment in the community via delivery of integrated primary care, mental health, substance abuse, and social services. Using blended public agency funding, traditional and nontraditional services are provided within a private, not-for-profit, managed care organization. Individualized, comprehensive care plans are developed by an MHSPY care manager, who works intensively with the family and the Care Planning Team to identify needs and resources. Data on clinical functioning are collected at baseline and every six months during the program. Service utilization and cost are measured on a quarterly basis. Family, youth, and agency satisfaction ratings are collected at disenrollment. Aggregate analyses based on four years of data show that MHSPY participants have improved clinical functioning, including significant reduction in risk to self and others. They also experience reduced service utilization and cost and high rates of family satisfaction.