High-density lipoprotein (HDL) as an indicator for alcohol use in a psychiatrically ill population.

AIMS: To examine the cross sectional and longitudinal associations between the Alcohol Use Disorders Identification Test-Concise (AUDIT-C) and differences in high-density lipoprotein (HDL) in a psychiatrically ill population. METHODS: Retrospective observational study using electronic health record data from a large healthcare system, of patients hospitalized for a mental health/substance use disorder (MH/SUD) from 1 July 2016 to 31 May 2023, who had a proximal AUDIT-C and HDL (N = 15 915) and the subset who had a repeat AUDIT-C and HDL 1 year later (N = 2915). Linear regression models examined the association between cross-sectional and longitudinal AUDIT-C scores and HDL, adjusting for demographic and clinical characteristics that affect HDL. RESULTS: Compared with AUDIT-C score = 0, HDL was higher among patients with greater AUDIT-C severity (e.g. moderate AUDIT-C score = 8.70[7.65, 9.75] mg/dl; severe AUDIT-C score = 13.02 [12.13, 13.90] mg/dL[95% confidence interval (CI)] mg/dl). The associations between cross-sectional HDL and AUDIT-C scores were similar with and without adjusting for patient demographic and clinical characteristics. HDL levels increased for patients with mild alcohol use at baseline and moderate or severe alcohol use at follow-up (15.06[2.77, 27.69] and 19.58[2.77, 36.39] mg/dL[95%CI] increase for moderate and severe, respectively). CONCLUSIONS: HDL levels correlate with AUDIT-C scores among patients with MH/SUD. Longitudinally, there were some (but not consistent) increases in HDL associated with increases in AUDIT-C. The increases were within range of typical year-to-year variation in HDL across the population independent of alcohol use, limiting the ability to use HDL as a longitudinal clinical indicator for alcohol use in routine care.

Self-Pay Outpatient Mental Health Care for Children and Adolescents, by Socioeconomic Status.

OBJECTIVE: Many parents struggle to find mental health care for their children, and many mental health clinicians do not accept insurance payments. The authors aimed to estimate the frequency and cost of self-pay psychotherapy and psychotropic medication management visits for youths and to determine how service use varies by family income. METHODS: A descriptive cross-sectional analysis was performed among youths ages 5-17 years in the 2018-2020 Medical Expenditure Panel Survey. Specialist visits included those with psychiatrists, psychologists, social workers, and mental health counselors or family therapists. RESULTS: Approximately one in five of 13,639 outpatient mental health specialist visits were self-pay, with psychologists (23% of visits) and social workers (24% of visits) most likely to see youths on a self-pay basis. Use of self-pay care was strongly associated with higher income, but even families earning <$28,000 per year utilized some self-pay care, at a median cost of $95 per visit. Self-pay visits were associated with slightly lower clinical need than insurance-covered visits, although this measure varied by income. CONCLUSIONS: The self-pay market for child mental health care potentially exacerbates inequities in access to care by burdening low-income families with high costs. Incentivizing mental health providers to participate in insurance for larger portions of their patient panels, for example, by increasing reimbursement rates and reducing paperwork, may help improve equitable access to mental health care. To the extent that reimbursement rates drive insurance acceptance, the frequency of self-pay mental health visits suggests that mental health services are underreimbursed relative to their benefit to patients and families.

Medical and Psychiatric Care Preceding the First Psychotic Disorder Diagnosis.

BACKGROUND: Individuals with psychotic symptoms experience substantial morbidity and have shortened life expectancies; early treatment may mitigate the worst effects. Understanding care preceding a first psychotic disorder diagnosis is critical to inform early detection and intervention. STUDY DESIGN: In this observational cohort study using comprehensive information from the Massachusetts All-Payer Claims Database, we identified the first psychotic disorder diagnosis in 2016, excluding those with historical psychotic disorder diagnoses in the prior 48 months among those continuous enrollment data. We reviewed visits, medications, and hospitalizations 2012-2016. We used logistic regression to examine characteristics associated with pre-diagnosis antipsychotic use. STUDY RESULTS: There were 2505 individuals aged 15-35 years (146 per 100 000 similarly aged individuals in the database) with a new psychotic disorder diagnosis in 2016. Most (97%) had at least one outpatient visit in the preceding 48 months; 89% had a prior mental health diagnosis unrelated to psychosis (eg, anxiety [60%], depression [60%]). Many received psychotropic medications (77%), including antipsychotic medications (46%), and 68% had a visit for injury or trauma during the preceding 48 months. Characteristics associated with filling an antipsychotic medication before the psychotic disorder diagnosis included male sex and Medicaid insurance at psychosis diagnosis. CONCLUSIONS: In this insured population of Massachusetts residents with a new psychotic disorder diagnosis, nearly all had some healthcare utilization, visits for injury or trauma were common, and nearly half filled an antipsychotic medication in the preceding 48 months. These patterns of care could represent either pre-disease signals, delays, or both in receiving a formal diagnosis.

Remodeling Broken Systems: Addressing the National Emergency in Child and Adolescent Mental Health.

A national emergency in child and adolescent mental health was declared in the United States in 2021 in the wake of the COVID-19 pandemic. This Open Forum discusses potential solutions to better support child and adolescent mental health by improving or expanding school-based mental health services, child psychiatry access programs, virtual mental health services, and new models of care (e.g., integrated youth services hubs and crisis stabilization units). The success of such programs is dependent on stable funding, strong leadership and accountability, robust and well-trained workforces, systems integration, and attention to health equity.

Value-Based Insurance Design: Clinically Nuanced Consumer Cost-Sharing for Mental Health Services.

BACKGROUND: While consumer cost-sharing is a widely used strategy to mitigate health care spending, numerous studies have demonstrated that even modest levels of out-of-pocket cost are associated with lower use of medical care, including clinically necessary, high-value services. Within mental health care, increases in cost-sharing are associated with reductions in use of mental health care and psychotropic medication use. Further, these reductions in mental health services and treatments can lead to downstream consequences including worsening of psychiatric illness and increased need for acute care and psychiatric hospitalization. Thus, there is a need for clinically informed solutions that explicitly balance the need for appropriate access to essential mental health services and treatments with growing fiscal pressures faced by public and private payers. Value-Based Insurance Design (VBID) describes a model where consumer cost-sharing is based on the potential clinical benefit rather than the price of a specific health care service or treatment. AIMS OF THE STUDY: Describe value-based insurance design and applications in mental health care. RESULTS, DISCUSSION AND IMPLICATIONS FOR HEALTH POLICIES: For over two decades, clinically nuanced VBID programs have been implemented in an effort to optimize the use of high-value health services and enhance equity through reduced consumer cost-sharing. Overall, the evidence suggests that VBID has demonstrated success in reducing consumer out-of-pocket costs associated with specific, high value services. By reducing financial barriers to essential clinical services and medications, VBID has potential to enhance equity. However, the impact of VBID on overall mental health care spending and clinical outcomes remains uncertain.

Tacking upwind: reducing spending among high-risk commercially insured patients.

OBJECTIVES: The study examined a commercial accountable care organization (ACO) population and then assessed the impact of an integrated care management program on medical spending and clinical event rates. STUDY DESIGN: Retrospective cohort study of high-risk individuals (n = 487) in a population of 365,413 individuals aged 18 to 64 years within the Mass General Brigham health system who were part of commercial ACO contracts with 3 large insurers between 2015 and 2019. METHODS: Using medical spending claims and other enrollment data, the study assessed the demographic and clinical characteristics, medical spending, and clinical event rates of patients in the ACO and its high-risk care management program. The study then examined the impact of the program using a staggered difference-in-difference design with individual-level fixed effects and compared outcomes of those who had entered the program with those of similar patients who had not entered. RESULTS: The commercially insured ACO population was healthy on average but included several hundred high-risk patients (n = 487). After adjustment, patients within the ACO's integrated care management program for high-risk patients had lower monthly medical spending (by $1361 per person per month) as well as lower emergency department visit and hospitalization rates compared with similar patients who had yet to start the program. Accounting for early ACO departure decreased the magnitude of the program effects as expected. CONCLUSIONS: Commercial ACO populations may be healthy on average but still include some high-risk patients. Identifying which patients might benefit from more intensive care management could be critical for reaping the potential savings.

Population turnover and leakage in commercial ACOs.

OBJECTIVES: Commercial accountable care organization (ACO) contracts attempt to mitigate spending growth, but past evaluations have been limited to continuously enrolled ACO members in health maintenance organization (HMO) plans, excluding many members. The objective of this study was to examine the magnitude of turnover and leakage within a commercial ACO. STUDY DESIGN: A historical cohort study using detailed information from multiple commercial ACO contracts within a large health care system between 2015 and 2019. METHODS: Individuals insured through 1 of the 3 largest commercial ACO contracts during the study period, 2015-2019, were included. We examined patterns of entry and exit and the characteristics that predicted remaining in the ACO compared with leaving the ACO. We also examined predictors of the amount of care delivered in the ACO compared with outside the ACO. RESULTS: Among the 453,573 commercially insured individuals in the ACO, approximately half left the ACO within the initial 24 months after entry. Approximately one-third of spending was for care occurring outside the ACO. Patients who remained in the ACO differed from those who left earlier, including being older, having a non-HMO plan, having lower predicted spending at entry, and having more medical spending for care performed within the ACO during the initial quarter of membership. CONCLUSIONS: Both turnover and leakage hamper the ability of ACOs to manage spending. Modifications that address potentially intrinsic vs avoidable sources of population turnover and increase patient incentives for care within vs outside of ACOs could help address medical spending growth within commercial ACO programs.

Focusing on Digital Research Priorities for Advancing the Access and Quality of Mental Health.

Digital mental health solutions are now well recognized as critical to solving the global mental health crisis. As research accelerates, it is now clear that solutions ranging from computer-based therapy programs to virtual reality headsets and smartphone apps to large language model chatbots are of interest, feasible, and hold exciting potential to improve mental health. This research should now consider the next generation of scientific and clinical questions regarding if these new approaches are equitable, valid, effective, implementable, efficacious, and even cost-effective. This paper outlines several of the new frontiers for the next generation of research and introduces JMIR Publications' partnership with the Society of Digital Psychiatry to further advance these aims.

Evaluating The Accuracy Of Medicare Risk Adjustment For Alzheimer's Disease And Related Dementias.

In 2020 Medicare reintroduced Alzheimer's disease and related dementias (ADRD) Hierarchical Condition Categories (HCCs) to risk-adjust Medicare Advantage and accountable care organization (ACO) payments. The potential for Medicare spending increases from this policy change are not well understood because the baseline accuracy of ADRD HCCs is uncertain. Using linked 2016-18 claims and electronic health record data from a large ACO, we evaluated the accuracy of claims-based ADRD HCCs against a reference standard of clinician-adjudicated disease. An estimated 7.5 percent of beneficiaries had clinician-adjudicated ADRD. Among those with ADRD HCCs, 34 percent did not have clinician-adjudicated disease. The false-negative and false-positive rates were 22.7 percent and 3.2 percent, respectively. Medicare spending for those with false-negative ADRD HCCs exceeded that of true positives by $14,619 per beneficiary. If, after the reintroduction of risk adjustment for ADRD, all false negatives were coded as having ADRD, expenditure benchmarks for beneficiaries with ADRD would increase by 9 percent. Monitoring ADRD coding could become challenging in the setting of concurrent incentives to decrease false-negative rates and increase false-positive rates.

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

BACKGROUND: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE: The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

Identifying Medicare Beneficiaries With Delirium.

BACKGROUND: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. METHODS: Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV). RESULTS: Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]. CONCLUSIONS: A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.

Mental Health-Related Emergency Department Visits Among Children During The Early COVID-19 Pandemic.

Objective: To measure univariate and covariate-adjusted trends in children's mental health-related emergency department (MH-ED) use across geographically diverse areas of the U.S. during the first wave of the Coronavirus-2019 (COVID-19) pandemic. Method: This is a retrospective, cross-sectional cohort study using electronic health records from four academic health systems, comparing percent volume change and adjusted risk of child MH-ED visits among children aged 3-17 years, matched on 36-week (3/18/19-11/25/19 vs. 3/16/20-11/22/20) and 12-week seasonal time intervals. Adjusted incidence rate ratios (IRR) were calculated using multivariate Poisson regression. Results: Visits declined during spring-fall 2020 (n = 3892 vs. n = 5228, -25.5%) and during spring (n = 1051 vs. n = 1839, -42.8%), summer (n = 1430 vs. n = 1469, -2.6%), and fall (n = 1411 vs. n = 1920, -26.5%), compared with 2019. There were greater declines among males (28.2% vs. females -22.9%), children 6-12-year (-28.6% vs. -25.9% for 3-5 years and -22.9% for 13-17 years), and Black children (-34.8% vs. -17.7% to -24.9%). Visits also declined for developmental disorders (-17.0%) and childhood-onset disorders (e.g., attention deficit and hyperactivity disorders; -18.0%). During summer-fall 2020, suicide-related visits rose (summer +29.8%, fall +20.4%), but were not significantly elevated from 2019 when controlling for demographic shifts. In contrast, MH-ED use during spring-fall 2020 was significantly reduced for intellectual disabilities (IRR 0.62 [95% CI 0.47-0.86]), developmental disorders (IRR 0.71 [0.54-0.92]), and childhood-onset disorders (IRR 0.74 [0.56-0.97]). Conclusions: The early pandemic brought overall declines in child MH-ED use alongside co-occurring demographic and diagnostic shifts. Children vulnerable to missed detection during instructional disruptions experienced disproportionate declines, suggesting need for future longitudinal research in this population.

Identifying Diagnoses of Schizophrenia Spectrum Disorder in Large Data Sets.

Objective: The authors used a large clinical data set to determine which index diagnoses of schizophrenia spectrum disorder were new diagnoses. Methods: Using the Massachusetts All-Payer Claims Database (2012­2016), the authors identified patients with a schizophrenia spectrum disorder diagnosis in 2016 (index diagnosis) and then reviewed patients' care histories for the previous 12, 24, 36, and 48 months to identify previous diagnoses. Logistic regression was used to examine patient characteristics associated with the index diagnosis being a new diagnosis. Results: Overall, 7,217 individuals ages 15­35 years had a 2016 diagnosis of schizophrenia spectrum disorder; 67.7% had at least 48 months of historical data. Among those with at least 48 months of care history, 23% had no previous diagnoses. Diagnoses from inpatient psychiatric admissions or among female or younger patients were more likely to represent new diagnoses, compared with diagnoses from most other diagnosis locations or among males or older age groups, and outpatient diagnoses were less likely to represent new diagnoses than were most other diagnosis settings. Reviewing 48 instead of 12 months of data reduced estimated rates of new diagnoses from 112 to 66 per 100,000 persons; historical diagnoses were detected for 61% and 77% of patients with 12 or 48 months of care history, respectively. Conclusions: Examining multiple years of patient history spanning all payers and providers is critical to identifying new schizophrenia spectrum disorder diagnoses in large data sets. Review of 48 months of care history resulted in lower rates of new schizophrenia spectrum disorder diagnoses than previously reported.

Hospital complications among older adults: Better processes could reduce the risk of delirium.

Using observational data and variation in hospital admissions across days of the week, we examined the association between ED boarding time and development of delirium within 72 hours of admission among patients aged 65+ years admitted to an inpatient neurology ward. We exploited a natural experiment created by potentially exogenous variation in boarding time across days of the week because of competition for the neurology floor beds. Using proportional hazard models adjusting for socio-demographic and clinical characteristics in a propensity score, we examined the time to delirium onset among 858 patients: 2/3 were admitted for stroke, with the remaining admitted for another acute neurologic event. Among all patients, 81.2% had at least one delirium risk factor in addition to age. All eligible patients received delirium prevention protocols upon admission to the floor and received at least one delirium screening event. While the clinical and social-demographic characteristics of admitted patients were comparable across days of the week, patients with ED arrival on Sunday or Tuesday were more likely to have had delayed floor admission (waiting time greater than 13 hours) and delirium (adjusted HR = 1.54, 95%CI:1.37-1.75). Delayed initiation of delirium prevention protocol appeared to be associated with greater risk of delirium within the initial 72 hours of a hospital admission.

Low Efficacy of Medication Shortage Clinical Decision Support Alerts.

OBJECTIVE: We examined clinical decision support (CDS) alerts designed specifically for medication shortages to characterize and assess provider behavior in response to these short-term clinical situations. MATERIALS AND METHODS: We conducted a retrospective analysis of the usage of medication shortage alerts (MSAs) that included at least one alternative medication suggestion and were active for 60 or more days during the 2-year study period, January 1, 2018 to December 31, 2019, in a large health care system. We characterized ordering provider behavior in response to inpatient MSAs. We then developed a linear regression model to predict provider response to alerts using the characteristics of the ordering provider and alert frequency groupings. RESULTS: During the study period, there were 67 MSAs in use that focused on 42 distinct medications in shortage. The MSAs suggested an average of 3.9 alternative medications. Adjusting for the different alerts, fellows (p = 0.004), residents (p = 0.03), and physician assistants (p = 0.02) were less likely to accept alerts on average compared with attending physicians. Further, female ordering clinicians (p < 0.001) were more likely to accept alerts on average compared with male ordering clinicians. CONCLUSION: Our findings demonstrate that providers tended to reject MSAs, even those who were sometimes flexible about their responses. The low overall acceptance rate supports the theory that alerts appearing at the time of order entry may have limited value, as they may be presented too late in the decision-making process. Though MSAs are designed to be attention-grabbing and higher impact than traditional CDS, our findings suggest that providers rarely change their clinical decisions when presented with these alerts.

"One size does not fit all" - lessons learned from a multiple-methods study of a resident wellness curriculum across sites and specialties.

BACKGROUND: There is growing recognition that wellness interventions should occur in context and acknowledge complex contributors to wellbeing, including individual needs, institutional and cultural barriers to wellbeing, as well as systems issues which propagate distress. The authors conducted a multiple-methods study exploring contributors to wellbeing for junior residents in diverse medical environments who participated in a brief resilience and stress-reduction curriculum, the Stress Management and Resiliency Training Program for Residents (SMART-R). METHODS: Using a waitlist-controlled design, the curriculum was implemented for post-graduate year (PGY)-1 or PGY-2 residents in seven residency programs across three sites. Every three months, residents completed surveys, including the Perceived Stress Scale-10, General Self-Efficacy Questionnaire, a mindfulness scale (CAMSR), and a depression screen (PHQ-2). Residents also answered free-text reflection questions about psychological wellbeing and health behaviors. RESULTS: The SMART-R intervention was not significantly associated with decreased perceived stress. Linear regression modeling showed that depression was positively correlated with reported stress levels, while male sex and self-efficacy were negatively correlated with stress. Qualitative analysis elucidated differences in these groups: Residents with lower self-efficacy, those with a positive depression screen, and/or female residents were more likely to describe experiencing lack of control over work. Residents with higher self-efficacy described more positive health behaviors. Residents with a positive depression screen were more self-critical, and more likely to describe negative personal life events. CONCLUSIONS: This curriculum did not significantly modify junior residents' stress. Certain subpopulations experienced greater stress than others (female residents, those with lower self-efficacy, and those with a positive depression screen). Qualitative findings from this study highlight universal stressful experiences early in residency, as well as important differences in experience of the learning environment among subgroups. Tailored wellness interventions that aim to support diverse resident sub-groups may be higher yield than a "one size fits all" approach. TRIAL REGISTRATION: NCT02621801 , Registration date: December 4, 2015 - Retrospectively registered.

Identifying Medicare beneficiaries with dementia.

BACKGROUND/OBJECTIVES: No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims. DESIGN: In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard. SETTING: Mass-General-Brigham Healthcare (MGB), Massachusetts, USA. PARTICIPANTS: From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. INTERVENTION: None. MEASUREMENTS: We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV). RESULTS: Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97). CONCLUSION: This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.