101 research outputs found

    Treatment‐Resistant Depression and Risk of Suicide

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99660/1/sltb12022.pd

    Modeling smoking-attributable mortality among adults with major depression in the United States

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    Tobacco-related health disparities disproportionately affect smokers with major depression (MD). Although tobacco simulation models have been applied to general populations, to date they have not considered populations with a comorbid mental health condition. We developed and calibrated a simulation model of smoking and MD comorbidity for the US adult population using the 2005–2018 National Surveys on Drug Use and Health. We use this model to evaluate trends in smoking prevalence, smoking-attributable mortality and life-years lost among adults with MD, and changes in smoking prevalence by mental health status from 2018–2060. The model integrates known interaction effects between smoking initiation and cessation, and MD onset and recurrence. We show that from 2018–2060, smoking prevalence will continue declining among those with current MD. In the absence of intervention, people with MD will be increasingly disproportionately affected by smoking compared to the general population; our model shows that the smoking prevalence ratio between those with current MD and those without a history of MD increases from 1.54 to 2.42 for men and from 1.81 to 2.73 for women during this time period. From 2018–2060, approximately 484,000 smoking-attributable deaths will occur among adults with current MD, leading to 11.3 million life-years lost. Ambitious tobacco control efforts could alter this trajectory. With aggressive public health efforts, up to 264,000 of those premature deaths could be avoided, translating into 7.5 million life years gained. This model can compare the relative health gains across different intervention strategies for smokers with MD

    Individual and neighborhood characteristics as predictors of depression symptom response

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149361/1/hesr13127_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149361/2/hesr13127.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149361/3/hesr13127-sup-0001-Authormatrix.pd

    In-Hospital and 1-Year Mortality Trends in a National Cohort of US Veterans with Acute Kidney Injury

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    BACKGROUND AND OBJECTIVES: AKI, a frequent complication among hospitalized patients, confers excess short- and long-term mortality. We sought to determine trends in in-hospital and 1-year mortality associated with AKI as defined by Kidney Disease Improving Global Outcomes consensus criteria. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This retrospective cohort study used data from the national Veterans Health Administration on all patients hospitalized from October 1, 2008 to September 31, 2017. AKI was defined by Kidney Disease Improving Global Outcomes serum creatinine criteria. In-hospital and 1-year mortality trends were analyzed in patients with and without AKI using Cox regression with year as a continuous variable. RESULTS: We identified 1,688,457 patients and 2,689,093 hospitalizations across the study period. Among patients with AKI, 6% died in hospital, and 28% died within 1 year. In contrast, in-hospital and 1-year mortality rates were 0.8% and 14%, respectively, among non-AKI hospitalizations. During the study period, there was a slight decline in crude in-hospital AKI-associated mortality (hazard ratio, 0.98 per year; 95% confidence interval, 0.98 to 0.99) that was attenuated after accounting for patient demographics, comorbid conditions, and acute hospitalization characteristics (adjusted hazard ratio, 0.99 per year; 95% confidence interval, 0.99 to 1.00). This stable temporal trend in mortality persisted at 1 year (adjusted hazard ratio, 1.00 per year; 95% confidence interval, 0.99 to 1.00). CONCLUSIONS: AKI associated mortality remains high, as greater than one in four patients with AKI died within 1 year of hospitalization. Over the past decade, there seems to have been no significant progress toward improving in-hospital or long-term AKI survivorship

    Innovative Solutions for State Medicaid Programs to Leverage Their Data, Build Their Analytic Capacity, and Create Evidence-Based Policy

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    As states have embraced additional flexibility to change coverage of and payment for Medicaid services, they have also faced heightened expectations for delivering high-value care. Efforts to meet these new expectations have increased the need for rigorous, evidence-based policy, but states may face challenges finding the resources, capacity, and expertise to meet this need. By describing state-university partnerships in more than 20 states, this commentary describes innovative solutions for states that want to leverage their own data, build their analytic capacity, and create evidence-based policy. From an integrated web-based system to improve long-term care to evaluating the impact of permanent supportive housing placements on Medicaid utilization and spending, these state partnerships provide significant support to their state Medicaid programs. In 2017, these partnerships came together to create a distributed research network that supports multi-state analyses. The Medicaid Outcomes Distributed Research Network (MODRN) uses a common data model to examine Medicaid data across states, thereby increasing the analytic rigor of policy evaluations in Medicaid, and contributing to the development of a fully functioning Medicaid innovation laboratory

    Validation of key behaviourally based mental health diagnoses in administrative data: suicide attempt, alcohol abuse, illicit drug abuse and tobacco use

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    <p>Abstract</p> <p>Background</p> <p>Observational research frequently uses administrative codes for mental health or substance use diagnoses and for important behaviours such as suicide attempts. We sought to validate codes (<it>International Classification of Diseases, 9<sup>th </sup>edition, clinical modification </it>diagnostic and E-codes) entered in Veterans Health Administration administrative data for patients with depression versus a gold standard of electronic medical record text ("chart notation").</p> <p>Methods</p> <p>Three random samples of patients were selected, each stratified by geographic region, gender, and year of cohort entry, from a VHA depression treatment cohort from April 1, 1999 to September 30, 2004. The first sample was selected from patients who died by suicide, the second from patients who remained alive on the date of death of suicide cases, and the third from patients with a new start of a commonly used antidepressant medication. Four variables were assessed using administrative codes in the year prior to the index date: suicide attempt, alcohol abuse/dependence, drug abuse/dependence and tobacco use.</p> <p>Results</p> <p>Specificity was high (≥ 90%) for all four administrative codes, regardless of the sample. Sensitivity was ≤75% and was particularly low for suicide attempt (≤ 17%). Positive predictive values for alcohol dependence/abuse and tobacco use were high, but barely better than flipping a coin for illicit drug abuse/dependence. Sensitivity differed across the three samples, but was highest in the suicide death sample.</p> <p>Conclusions</p> <p>Administrative data-based diagnoses among VHA records have high specificity, but low sensitivity. The accuracy level varies by different diagnosis and by different patient subgroup.</p

    Factors Influencing Cost-Related Nonadherence to Medication in Older Adults: A Conceptually Based Approach

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    Although multiple noncost factors likely influence a patient's propensity to forego treatment in the face of cost pressures, little is known about how patients' sociodemographic characteristics, physical and behavioral health comorbidities, and prescription regimens influence cost-related nonadherence (CRN) to medications. We sought to determine both financial and nonfinancial factors associated with CRN in a nationally representative sample of older adults.We used a conceptual model developed by Piette and colleagues that describes financial and nonfinancial factors that could increase someone's risk of CRN, including income, comorbidities, and medication regimen complexity. We used data from the 2004 wave of the Health and Retirement Study and the 2005 HRS Prescription Drug Study to examine the influence of factors within each of these domains on measures of CRN (including not filling, stopping, or skipping doses) in a nationally representative sample of Americans age 65+ in 2005.Of the 3071 respondents who met study criteria, 20% reported some form of CRN in 2005. As in prior studies, indicators of financial stress such as higher out-of-pocket payments for medications and lower net worth were significantly associated with CRN in multivariable analyses. Controlling for these economic pressures, relatively younger respondents (ages 65–74) and depressive symptoms were consistent independent risk factors for CRN.Noncost factors influenced patients' propensity to forego treatment even in the context of cost concerns. Future research encompassing clinician and health system factors should identify additional determinants of CRN beyond patients' cost pressures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78680/1/j.1524-4733.2009.00679.x.pd

    Trends in Use of Medication to Treat Opioid Use Disorder During the COVID-19 Pandemic in 10 State Medicaid Programs

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    Federal and state agencies granted temporary regulatory waivers to prevent disruptions in access to medication for opioid use disorder (MOUD) during the COVID-19 pandemic, including expanding access to telehealth for MOUD. Little is known about changes in MOUD receipt and initiation among Medicaid enrollees during the pandemic.To examine changes in receipt of any MOUD, initiation of MOUD (in-person vs telehealth), and the proportion of days covered (PDC) with MOUD after initiation from before to after declaration of the COVID-19 public health emergency (PHE).This serial cross-sectional study included Medicaid enrollees aged 18 to 64 years in 10 states from May 2019 through December 2020. Analyses were conducted from January through March 2022.Ten months before the COVID-19 PHE (May 2019 through February 2020) vs 10 months after the PHE was declared (March through December 2020).Primary outcomes included receipt of any MOUD and outpatient initiation of MOUD via prescriptions and office- or facility-based administrations. Secondary outcomes included in-person vs telehealth MOUD initiation and PDC with MOUD after initiation.Among a total of 8 167 497 Medicaid enrollees before the PHE and 8 181 144 after the PHE, 58.6% were female in both periods and most enrollees were aged 21 to 34 years (40.1% before the PHE; 40.7% after the PHE). Monthly rates of MOUD initiation, representing 7% to 10% of all MOUD receipt, decreased immediately after the PHE primarily due to reductions in in-person initiations (from 231.3 per 100 000 enrollees in March 2020 to 171.8 per 100 000 enrollees in April 2020) that were partially offset by increases in telehealth initiations (from 5.6 per 100 000 enrollees in March 2020 to 21.1 per 100 000 enrollees in April 2020). Mean monthly PDC with MOUD in the 90 days after initiation decreased after the PHE (from 64.5% in March 2020 to 59.5% in September 2020). In adjusted analyses, there was no immediate change (odds ratio [OR], 1.01; 95% CI, 1.00-1.01) or change in the trend (OR, 1.00; 95% CI, 1.00-1.01) in the likelihood of receipt of any MOUD after the PHE compared with before the PHE. There was an immediate decrease in the likelihood of outpatient MOUD initiation (OR, 0.90; 95% CI, 0.85-0.96) and no change in the trend in the likelihood of outpatient MOUD initiation (OR, 0.99; 95% CI, 0.98-1.00) after the PHE compared with before the PHE.In this cross-sectional study of Medicaid enrollees, the likelihood of receipt of any MOUD was stable from May 2019 through December 2020 despite concerns about potential COVID-19 pandemic–related disruptions in care. However, immediately after the PHE was declared, there was a reduction in overall MOUD initiations, including a reduction in in-person MOUD initiations that was only partially offset by increased use of telehealth
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