Racial and Ethnic Differences in Telemedicine Use.

Importance: Individuals of racial and ethnic minority groups may be less likely to use telemedicine in part due to lack of access to technology (ie, digital divide). To date, some studies have found less telemedicine use by individuals of racial and ethnic minority groups compared with White individuals, and others have found the opposite. What explains these different findings is unclear. Objective: To quantify racial and ethnic differences in the receipt of telemedicine and total visits with and without accounting for demographic and clinical characteristics and geography. Design, Setting, and Participants: This cross-sectional study included individuals who were continuously enrolled in traditional Medicare from March 2020 to February 2022 or until death. Exposure: Race and ethnicity, which was categorized as Black non-Hispanic, Hispanic, White non-Hispanic, other (defined as American Indian/Pacific Islander, Alaska Native, and Asian), and unknown/missing. Main Outcomes and Measures: Total telemedicine visits (audio-video or audio); total visits (telemedicine or in-person) per individual during the study period. Multivariable models were used that sequentially adjusted for demographic and clinical characteristics and geographic area to examine their association with differences in telemedicine and total visit utilization by documented race and ethnicity. Results: In this national sample of 14 305 819 individuals, 7.4% reported that they were Black, 5.6% Hispanic, and 4.2% other race. In unadjusted results, compared with White individuals, Black individuals, Hispanic individuals, and individuals of other racial groups had 16.7 (95% CI, 16.1-17.3), 32.9 (95% CI, 32.3-33.6), and 20.9 (95% CI, 20.2-21.7) more telemedicine visits per 100 beneficiaries, respectively. After adjustment for clinical and demographic characteristics and geography, compared with White individuals, Black individuals, Hispanic individuals, and individuals of other racial groups had 7.9 (95% CI, -8.5 to -7.3), 13.2 (95% CI, -13.9 to -12.6), and 9.2 (95% CI, -10.0 to -8.5) fewer telemedicine visits per 100 beneficiaries, respectively. In unadjusted and fully adjusted models, and in 2019 and the second year of the COVID-19 pandemic, Black individuals, Hispanic individuals, and individuals of other racial groups continued to have fewer total visits than White individuals. Conclusions and Relevance: The results of this cross-sectional study of US Medicare enrollees suggest that although nationally, Black individuals, Hispanic individuals, and individuals of other racial groups received more telemedicine visits during the pandemic and disproportionately lived in geographic regions with higher telemedicine use, after controlling for geographic region, Black individuals, Hispanic individuals, and individuals of other racial groups received fewer telemedicine visits than White individuals.

Classification Algorithm to Distinguish Between Type 1 and Type 2 Myocardial Infarction in Administrative Claims Data.

BACKGROUND: Type 2 myocardial infarction (T2MI) and type 1 myocardial infarction (T1MI) differ with respect to demographics, comorbidities, treatments, and clinical outcomes. Reliable quality and outcomes assessment depends on the ability to distinguish between T1MI and T2MI in administrative claims data. As such, we aimed to develop a classification algorithm to distinguish between T1MI and T2MI that could be applied to claims data. METHODS: Using data for beneficiaries in a Medicare accountable care organization contract in a large health care system in New England, we examined the distribution of MI diagnosis codes between 2018 to 2021 and the patterns of care and coding for beneficiaries with a hospital discharge diagnosis International Classification of Diseases, Tenth Revision code for T2MI, compared with those for T1MI. We then assessed the probability that each hospitalization was for a T2MI versus T1MI and examined care occurring in 2017 before the introduction of the T2MI code. RESULTS: After application of inclusion and exclusion criteria, 7759 hospitalizations for myocardial infarction remained (46.5% T1MI and 53.5% T2MI; mean age, 79±10.3 years; 47% female). In the classification algorithm, female gender (odds ratio, 1.26 [95% CI, 1.11-1.44]), Black race relative to White race (odds ratio, 2.48 [95% CI, 1.76-3.48]), and diagnoses of COVID-19 (odds ratio, 1.74 [95% CI, 1.11-2.71]) or hypertensive emergency (odds ratio, 1.46 [95% CI, 1.00-2.14]) were associated with higher odds of the hospitalization being for T2MI versus T1MI. When applied to the testing sample, the C-statistic of the full model was 0.83. Comparison of classified T2MI and observed T2MI suggest the possibility of substantial misclassification both before and after the T2MI code. CONCLUSIONS: A simple classification algorithm appears to be able to differentiate between hospitalizations for T1MI and T2MI before and after the T2MI code was introduced. This could facilitate more accurate longitudinal assessments of acute myocardial infarction quality and outcomes.

Healthcare Access for a Diverse Population with Schizophrenia Following the Onset of the COVID-19 Pandemic.

COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic's impacts among New York State's Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions-while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities' heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises.

Use of Telemedicine and Quality of Care Among Medicare Enrollees With Serious Mental Illness.

Importance: During the COVID-19 pandemic, a large fraction of mental health care was provided via telemedicine. The implications of this shift in care for use of mental health service and quality of care have not been characterized. Objective: To compare changes in care patterns and quality during the first year of the pandemic among Medicare beneficiaries with serious mental illness (schizophrenia or bipolar I disorder) cared for at practices with higher vs lower telemedicine use. Design, Setting, and Participants: In this cohort study, Medicare fee-for-service beneficiaries with schizophrenia or bipolar I disorder were attributed to specialty mental health practices that delivered the majority of their mental health care in 2019. Practices were categorized into 3 groups based on the proportion of telemental health visits provided during the first year of the pandemic (March 2020-February 2021): lowest use (0%-49%), middle use (50%-89%), or highest use (90%-100%). Across the 3 groups of practices, differential changes in patient outcomes were calculated from the year before the pandemic started to the year after. These changes were also compared with differential changes from a 2-year prepandemic period. Analyses were conducted in November 2022. Exposure: Practice-level use of telemedicine during the first year of the COVID-19 pandemic. Main Outcomes and Measures: The primary outcome was the total number of mental health visits (telemedicine plus in-person) per person. Secondary outcomes included the number of acute hospital and emergency department encounters, all-cause mortality, and quality outcomes, including adherence to antipsychotic and mood-stabilizing medications (as measured by the number of months of medication fills) and 7- and 30-day outpatient follow-up rates after discharge for a mental health hospitalization. Results: The pandemic cohort included 120 050 Medicare beneficiaries (mean [SD] age, 56.5 [14.5] years; 66 638 females [55.5%]) with serious mental illness. Compared with prepandemic changes and relative to patients receiving care at practices with the lowest telemedicine use: patients receiving care at practices in the middle and highest telemedicine use groups had 1.11 (95% CI, 0.45-1.76) and 1.94 (95% CI, 1.28-2.59) more mental health visits per patient per year (or 7.5% [95% CI, 3.0%-11.9%] and 13.0% [95% CI, 8.6%-17.4%] more mental health visits per year, respectively). Among patients of practices with middle and highest telemedicine use, changes in adherence to antipsychotic and mood-stabilizing medications were -0.4% (95% CI, -1.3% to 0.5%) and -0.1% (95% CI, -1.0% to 0.8%), and hospital and emergency department use for any reason changed by 2.4% (95% CI, -1.5% to 6.2%) and 2.8% (95% CI, -1.2% to 6.8%), respectively. There were no significant differential changes in postdischarge follow-up or mortality rates according to the level of telemedicine use. Conclusions and Relevance: In this cohort study of Medicare beneficiaries with serious mental illness, patients receiving care from practices that had a higher level of telemedicine use during the COVID-19 pandemic had more mental health visits per year compared with prepandemic levels, with no differential changes in other observed quality metrics over the same period.

Relationship Between In-Hospital Adverse Events and Hospital Performance on 30-Day All-cause Mortality and Readmission for Patients With Heart Failure.

BACKGROUND: Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events. METHODS: This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics. RESULTS: The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44]). CONCLUSIONS: Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF.

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.

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.

Premium tax credits in the American Rescue Plan and off-marketplace enrollees.

OBJECTIVES: The 2021 American Rescue Plan Act (ARPA) increased the availability and magnitude of premium tax credits (PTCs) for consumers purchasing individual marketplace plans in 2021-2022. Millions currently purchase PTC-ineligible plans off of the marketplace. We estimate the proportion of off-marketplace enrollees who would be eligible for the expanded PTCs under ARPA, calculate PTC amounts for eligible enrollees, and examine factors influencing plan choice that could inform outreach efforts. STUDY DESIGN: We analyzed data from a survey of a random sample of off-marketplace enrollees in California in 2017 (n = 829). METHODS: Using survey data including self-reported income, household size, and employment status combined with 2021 benchmark premium data from Covered California, we estimate eligibility for PTCs and potential PTC amounts under ARPA among off-marketplace enrollees. We adjust for both survey design weights and poststratification weights. RESULTS: Among off-marketplace enrollees, we estimate that approximately 12% are potentially ineligible for PTCs because they reported incomes less than 100% of the poverty level or because they had access to employer-sponsored coverage for their family through themselves or their partner. The median annual PTC in 2021 for eligible off-marketplace enrollees was $311 but varied greatly by age, family or individual plan, and household income (5%-95% range, $0-$14,836). In 2017, 69% of off-marketplace enrollees were unaware that they had to enroll in marketplace plans to receive PTCs, and 51% received enrollment assistance from insurance brokers. CONCLUSIONS: These findings suggest the need for targeted outreach to encourage off-marketplace enrollees to switch to marketplace plans.

Statistical Approaches for Quantifying the Quality of Neurosurgical Care.

BACKGROUND: Quantifying quality of health care can provide valuable information to patients, providers, and policy makers. However, the observational nature of measuring quality complicates assessments. METHODS: We describe a conceptual model for defining quality and its implications about the data collected, how to make inferences about quality, and the assumptions required to provide statistically valid estimates. Twenty-one binary or polytomous quality measures collected from 101,051 adult Medicaid beneficiaries aged 18-64 years with schizophrenia from 5 U.S. states show methodology. A categorical principal components analysis establishes dimensionality of quality, and item response theory models characterize the relationship between each quality measure and a unidimensional quality construct. Latent regression models estimate racial/ethnic and geographic quality disparities. RESULTS: More than 90% of beneficiaries filled at least 1 antipsychotic prescription and 19% were hospitalized for schizophrenia during a 12-month observational period in our multistate cohort with approximately 2/3 nonwhite beneficiaries. Four quality constructs emerged: inpatient, emergency room, pharmacologic/ambulatory, and ambulatory only. Using a 2-parameter logistic model, pharmacologic/ambulatory care quality varied from -2.35 to 1.26 (higher = better quality). Black and Latinx beneficiaries had lower pharmacologic/ambulatory quality compared with whites. Race/ethnicity modified the association of state and pharmacologic/ambulatory care quality in latent regression modeling. Average quality ranged from -0.28 (95% confidence interval, -2.15 to 1.04) for blacks in New Jersey to 0.46 [95% confidence interval, -0.89 to 1.40] for whites in Michigan. CONCLUSIONS: By combining multiple quality measures using item response theory models, a composite measure can be estimated that has more statistical power to detect differences among subjects than the observed mean per subject.

Analysis of Hospital-Level Readmission Rates and Variation in Adverse Events Among Patients With Pneumonia in the United States.

Importance: It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events. Objective: To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events. Design, Setting, and Participants: This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019. Exposures: Patients hospitalized for pneumonia. Main Outcomes and Measures: Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges. Results: The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively. Conclusions and Relevance: Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.

Measuring the quality of dental care among privately insured children in the United States.

OBJECTIVE: To examine whether quality of dental care varies by age and over time and whether community-level characteristics explain these patterns. DATA SOURCE: Deidentified medical and dental claims from a commercial insurer from January 2015 to December 2019. STUDY DESIGN: A retrospective cohort study. The primary outcome was a composite quality score, derived from seven dental quality measures (DQMs), with higher values corresponding to better quality. Hierarchical regression models identified person- and zip code-level factors associated with the quality. DATA COLLECTION/EXTRACTION METHODS: Continuously enrolled US dental insurance beneficiaries younger than 21 years of age. PRINCIPAL FINDINGS: Quality was assessed for 4.88 million person-years covering 1.31 million persons. Overall quality slightly improved over time, mostly driven by substantial improvements among children aged 0-5 years by 0.153 points/year (95% confidence interval [CI]:0.151, 0.156). Quality was poorest and declined over time among adolescents with only 20.5% of DQMs met as compared to 42.6% among aged 0-5 years in 2019. Dental professional shortage, median household income, percentages of African Americans, unemployed, and less-educated populations at the zip code level were associated with the composite score. CONCLUSION: Quality of dental care among adolescents remains low, and place of residence influenced the quality. Increasing the supply of dentists and oral health promotion strategies targeting adolescents and low-performing localities should be explored.

Significance and Factors Associated with Antipsychotic Polypharmacy Utilization Among Publicly Insured US Adults.

Antipsychotic polypharmacy (APP) lacks evidence of effectiveness in the care of schizophrenia or other disorders for which antipsychotic drugs are indicated, also exposing patients to more risks. Authors assessed APP prevalence and APP association with beneficiary race/ethnicity and payer among publicly-insured adults regardless of diagnosis. Retrospective repeated panel study of fee-for-service (FFS) Medicare, Medicaid, and dually-eligible white, black, and Latino adults residing in California, Georgia, Iowa, Mississippi, Oklahoma, South Dakota, or West Virginia, filling antipsychotic prescriptions between July 2008 and June 2013. Primary outcome was any monthly APP utilization. Across states and payers, 11% to 21% of 397,533 antipsychotic users and 12% to 19% of 9,396,741 person-months had some APP utilization. Less than 50% of person-months had a schizophrenia diagnosis and up to 19% had no diagnosed mental illness. Payer modified race/ethnicity effects on APP utilization only in CA; however, the odds of APP utilization remained lower for minorities than for whites. Elsewhere, the odds varied by race/ethnicity only in OK, with Latinos having lower odds than whites (odds ratio 0.76; 95% confidence interval 0.60-0.96). The odds of APP utilization varied by payer in several study states, with odds generally higher for Dual eligibles, although the differences were generally small; the odds also varied by year (lower at study end). APP was frequently utilized but mostly declined over time. APP utilization patterns varied across states, with no consistent association with race/ethnicity and small payer effects. Greater use of APP-reducing strategies are needed, particularly among non-schizophrenia populations.

Factors Associated With Off-Label Utilization of Second-Generation Antipsychotics Among Publicly Insured Adults.

OBJECTIVE: Off-label utilization of second-generation antipsychotic medications may expose patients to significant risks. The authors examined the prevalence, temporal trends, and factors associated with off-label utilization of second-generation antipsychotics among publicly insured adults. METHODS: A retrospective repeated panel was used to examine monthly off-label utilization of second-generation antipsychotics among fee-for-service Medicare, Medicaid, and dually eligible White, Black, and Latino adult beneficiaries filling prescriptions for second-generation antipsychotics in California, Georgia, Mississippi, and Oklahoma from July 2008 through June 2013. RESULTS: Among 301,367 users of second-generation antipsychotics, between 36.5% and 41.9% had utilization that was always off-label. Payer did not modify effects of race-ethnicity on off-label utilization. Compared with Whites, Blacks had lower monthly odds of off-label utilization in all four states, and Latinos had lower odds of utilization in California and Georgia. Payer was associated with off-label utilization in California, Mississippi, and Oklahoma. California Medicaid beneficiaries were 1.12 (95% confidence interval=1.10-1.13) times as likely as dually eligible beneficiaries to have off-label utilization. Off-label utilization increased relative to the baseline year in all states, but a downward trend followed in three states. CONCLUSIONS: Off-label utilization of second-generation antipsychotics was prevalent despite the drugs' cardiometabolic risks and little evidence of their effectiveness. The lower likelihood of off-label utilization among patients from racial-ethnic minority groups might stem from prescribers' efforts to minimize risks, given a higher baseline risk for these groups, or from disparities-associated factors. Variation among payers suggests that payer policies can affect off-label utilization.

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.

Quality of primary health care in China: challenges and recommendations.

China has substantially increased financial investment and introduced favourable policies for strengthening its primary health care system with core responsibilities in preventing and managing chronic diseases such as hypertension and emerging infectious diseases such as coronavirus disease 2019 (COVID-19). However, widespread gaps in the quality of primary health care still exist. In this Review, we aim to identify the causes for this poor quality, and provide policy recommendations. System challenges include: the suboptimal education and training of primary health-care practitioners, a fee-for-service payment system that incentivises testing and treatments over prevention, fragmentation of clinical care and public health service, and insufficient continuity of care throughout the entire health-care system. The following recommendations merit consideration: (1) enhancement of the quality of training for primary health-care physicians, (2) establishment of performance accountability to incentivise high-quality and high-value care; (3) integration of clinical care with the basic public health services, and (4) strengthening of the coordination between primary health-care institutions and hospitals. Additionally, China should consider modernising its primary health-care system through the establishment of a learning health system built on digital data and innovative technologies.

Assessment of Second-Generation Diabetes Medication Initiation Among Medicare Enrollees From 2007 to 2015.

Importance: Little is known about how new and expensive drugs diffuse into practice affects health care costs. Objective: To describe the variation in second-generation diabetes drug use among Medicare enrollees between 2007 and 2015. Design, Setting, and Participants: This population-based, cross-sectional study included data from 100% of Medicare Parts A, B, and D enrollees who first received diabetes drug therapy from January 1, 2007, to December 31, 2015. Patients with type 1 diabetes were excluded. Data were analyzed beginning in the spring of 2018, and revisions were completed in 2019. Exposures: For each patient, the initial diabetes drug choice was determined; drugs were classified as first generation (ie, approved before 2000) or second generation (ie, approved after 2000, including dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide-1 [GLP-1] receptor agonists, and sodium-glucose cotransporter-2 [SGLT-2] inhibitors). Main Outcomes and Measures: The primary outcome was the between-practice variation in use of second-generation diabetes drugs between 2007 and 2015. Practices with use rates of second-generation diabetes drugs more than 1 SD above the mean were considered high prescribing, while those with use rates more than 1 SD below the mean were considered low prescribing. Results: Among 1 182 233 patients who initiated diabetes drug therapy at 42 977 practices between 2007 and 2015, 1 104 718 (93.4%) were prescribed a first-generation drug (mean [SD] age, 75.4 [6.7] years; 627 134 [56.8%] women) and 77 515 (6.6%) were prescribed a second-generation drug (mean [SD] age, 76.5 [7.2] years; 44 697 [57.7%] women). By December 2015, 22 457 practices (52.2%) had used DPP-4 inhibitors once, compared with 3593 practices (8.4%) that had used a GLP-1 receptor agonist once. Furthermore, 17 452 practices (40.6%) were using DPP-4 inhibitors in 10% of eligible patients, while 1286 practices (3.0%) were using GLP-1 receptor agonists in 10% of eligible patients, and SGLT-2 inhibitors, available after March 2013, were used at least once by 1716 practices (4.0%) and used in 10% of eligible patients by 872 practices (2.0%) by December 2015. According to Poisson random-effect regression models, beneficiaries in high-prescribing practices were more than 3-fold more likely to receive DPP-4 inhibitors (relative risk, 3.55 [95% CI, 3.42-3.68]), 24-fold more likely to receive GLP-1 receptor agonists (relative risk, 24.06 [95% CI, 14.14-40.94]) and 60-fold more likely to receive SGLT-2 inhibitors (relative risk, 60.41 [95% CI, 15.99-228.22]) compared with beneficiaries in low-prescribing practices. Conclusions and Relevance: These findings suggest that there was substantial between-practice variation in the use of second-generation diabetes drugs between 2007 and 2015, with a concentration of use among a few prescribers and practices responsible for much of the early diffusion.

Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States.

Importance: Studies have shown that adverse events are associated with increasing inpatient care expenditures, but contemporary data on the association between expenditures and adverse events beyond inpatient care are limited. Objective: To evaluate whether hospital-specific adverse event rates are associated with hospital-specific risk-standardized 30-day episode-of-care Medicare expenditures for fee-for-service patients discharged with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Design, Setting, and Participants: This cross-sectional study used the 2011 to 2016 hospital-specific risk-standardized 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services and medical record-abstracted in-hospital adverse event data from the Medicare Patient Safety Monitoring System. The setting was acute care hospitals treating at least 25 Medicare fee-for-service patients for AMI, HF, or pneumonia in the United States. Participants were Medicare fee-for-service patients 65 years or older hospitalized for AMI, HF, or pneumonia included in the Medicare Patient Safety Monitoring System in 2011 to 2016. The dates of analysis were July 16, 2017, to May 21, 2018. Main Outcomes and Measures: Hospitals' risk-standardized 30-day episode-of-care expenditures and the rate of occurrence of adverse events for which patients were at risk. Results: The final study sample from 2194 unique hospitals included 44 807 patients (26.1% AMI, 35.6% HF, and 38.3% pneumonia) with a mean (SD) age of 79.4 (8.6) years, and 52.0% were women. The patients represented 84 766 exposures for AMI, 96 917 exposures for HF, and 109 641 exposures for pneumonia. Patient characteristics varied by condition but not by expenditure category. The mean (SD) risk-standardized expenditures were $22 985 ($1579) for AMI, $16 020 ($1416) for HF, and $16 355 ($1995) for pneumonia per hospitalization. The mean risk-standardized rates of occurrence of adverse events for which patients were at risk were 3.5% (95% CI, 3.4%-3.6%) for AMI, 2.5% (95% CI, 2.5%-2.5%) for HF, and 3.0% (95% CI, 2.9%-3.0%) for pneumonia. An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge. Conclusions and Relevance: Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.

Association Between Subsequent Hospitalizations and Recurrent Acute Myocardial Infarction Within 1 Year After Acute Myocardial Infarction.

Background Patients who survive acute myocardial infarction (AMI) are at high risk for recurrence. We determined whether rehospitalizations after AMI further increased risk of recurrent AMI. Methods and Results The study included Medicare fee-for-service patients aged ≥65 years discharged alive after AMI from acute-care hospitals in fiscal years 2009-2014. The outcome was recurrent AMI within 1 year of the index AMI. The Clinical Classifications Software (CCS) was used to classify rehospitalizations into disease categories. A Cox regression model was fit accounting for CCS-specific hospitalizations as time-varying variables and patient characteristics at discharge for the index AMI, adjusting for the competing risk of death. The rate of 1-year recurrent AMI was 5.3% (95% CI, 5.27%-5.41%), and median (interquartile range) time from discharge to recurrent AMI was 115 (34-230) days. Eleven disease categories (diabetes mellitus, anemia, hypertension, coronary atherosclerosis, chest pain, heart failure, pneumonia, chronic obstructive pulmonary disease, gastrointestinal hemorrhage, renal failure, complication of implant or graft) were associated with increased risk of recurrent AMI. Septicemia was associated with lower recurrence risk. Hazard ratios ranged from 1.6 (95% CI, 1.55-1.70, heart failure) to 1.1 (95% CI, 1.04-1.25, pneumonia) to 0.6 (95% CI, 0.58-0.71, septicemia). Conclusions Patient risk of recurrent AMI changed based on the occurrence of hospitalizations after the index AMI. Improving post-acute care to prevent unplanned rehospitalizations, especially rehospitalizations for chronic diseases, and extending the focus of outcomes measures to condition-specific rehospitalizations within 30 days and beyond is important for the secondary prevention of AMI.

Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data.

Background More than 600 000 coronary stents are implanted during percutaneous coronary interventions (PCIs) annually in the United States. Because no real-world surveillance system exists to monitor their long-term safety, claims data are often used for this purpose. The extent to which adverse events identified with claims data can be reasonably attributed to a specific medical device is uncertain. Methods and Results We used deterministic matching to link the NCDR (National Cardiovascular Data Registry) CathPCI Registry to Medicare fee-for-service claims for patients aged ≥65 years who underwent PCI with drug-eluting stents (DESs) between July 1, 2009 and December 31, 2013. We identified subsequent PCIs within 1 year of the index procedure in Medicare claims as potential safety events. We linked these subsequent PCIs back to the NCDR CathPCI Registry to ascertain how often the revascularization could be reasonably attributed to the same coronary artery as the index PCI (ie, target vessel revascularization). Of 415 306 DES placements in 368 194 patients, 33 174 repeat PCIs were identified in Medicare claims within 1 year. Of these, 28 632 (86.3%) could be linked back to the NCDR CathPCI Registry; 16 942 (51.1% of repeat PCIs) were target vessel revascularizations. Of these, 8544 (50.4%) were within a previously placed DES: 7652 for in-stent restenosis and 1341 for stent thrombosis. Of 16 176 patients with a claim for acute myocardial infarction in the follow-up period, 4446 (27.5%) were attributed to the same coronary artery in which the DES was implanted during the index PCI (ie, target vessel myocardial infarction). Of 24 288 patients whose death was identified in claims data, 278 (1.1%) were attributed to the same coronary artery in which the DES was implanted during the index PCI. Conclusions Most repeat PCIs following DES stent implantation identified in longitudinal claims data could be linked to real-world registry data, but only half could be reasonably attributed to the same coronary artery as the index procedure. Attribution among those with acute myocardial infarction or who died was even less frequent. Safety signals identified using claims data alone will require more in-depth examination to accurately assess stent safety.