Association between degree of exposure to the Hospital Value Based Purchasing Program and 30-day mortality: experience from the first four years of Medicare's pay-for-performance program

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.

Association of the Home Health Value-Based Purchasing Model With Quality, Utilization, and Medicare Payments After the First 5 Years

Importance

The original Home Health Value-Based Purchasing (HHVBP) model provided financial incentives to home health agencies for quality improvement in 9 randomly selected US states.

Objective

To evaluate quality, utilization, and Medicare payments for home health patients in HHVBP states compared with those in comparison states.

Design, Setting, and Participants

This cohort study was conducted in 2021 with secondary data from January 2013 to December 2020. A difference-in-differences design and multivariate linear regression were used to compare outcomes for Medicare and Medicaid beneficiaries who received home health care in HHVBP states with those in 41 comparison states during 3 years of preintervention (2013-2015) and the subsequent 5 years (2016-2020).

Exposures

Home health care provided by a home health agency in HHVBP states and comparison states.

Main Outcomes and Measures

Utilization (unplanned hospitalizations, emergency department visits, skilled nursing facility [SNF] visits) for Medicare beneficiaries within 60 days of beginning home health, Medicare payments during and 37 days after home health episodes, and quality of care (functional status, patient experience) during home health episodes.

Results

Among 34 058 796 home health episodes (16 584 870 beneficiaries; mean [SD] age of 76.6 [11.7] years; 60.5% female; 11.2% Black non-Hispanic; 79.5% White non-Hispanic) from January 2016 to December 2020, 22.6% were in HHVBP states and 77.4% were in non-HHVBP states. For the HHVBP and non-HHVBP groups, 60.4% and 61.0% of episodes were provided to female patients; 10.0% and 13.6% were provided to Black non-Hispanic patients, and 82.4% and 75.2% were provided to White non-Hispanic patients, respectively. Unplanned hospitalizations decreased by 0.15 percentage points (95% CI, -0.30 to -0.01) more in HHVBP states, a 1.0% decline compared with 15.7% at baseline. The use of SNFs decreased by 0.34 percentage points (95% CI, -0.40 to -0.27) more in HHVBP states, a 6.9% decline compared with the 4.9% baseline average. There was an association between HHVBP and a reduction in average Medicare payments per day of $2.17 (95% CI, -$3.67 to -$0.68) in HHVBP states, primarily associated with reduced inpatient and SNF services, which corresponded to an average annual Medicare savings of $190 million. There was greater functional improvement in HHVBP states than comparison states and no statistically significant change in emergency department use or most measures of patient experience.

Conclusions and Relevance

In this cohort study, the HHVBP model was associated with lower Medicare payments that were associated with lower utilization of inpatient and SNF services, with better or similar quality of care.

Employer-Led Strategies to Improve the Value of Health Spending: A Systematic Review

OBJECTIVE

To systematically review studies that evaluated the impact of employer-led efforts in the United States to improve the value of health spending, where employers have implemented changes to their health benefits to reduce costs while improving or maintaining quality.

METHODS

We included all studies of employer-led health benefit strategies that reported outcomes for both employer health spending and employee health outcomes.

RESULTS

Our search returned 44 studies of employer health benefit changes that included measures of both health spending and quality. The most promising efforts were those that lowered or eliminated cost sharing for primary care or medications for chronic illnesses. High deductible health plans with a savings option appeared less promising.

CONCLUSIONS

More research is needed on the characteristics and contexts in which these benefit changes were implemented, and on actions that address employers' current concerns.

Predictors of CD4 count changes over time among children who initiated highly active antiretroviral therapy in Ethiopia

Introduction

Human immunodeficiency virus (HIV) infection results in a gradual depletion of immune function, particularly CD4 cells. The CD4 assessment plays a significant role in assessing treatment responses and clinical decision-making for patients on combination antiretroviral therapy (ART) in resource-limited settings. However, new data on CD4 count changes are scarce; the volatility of CD4 counts after initiation of ART over time remains largely uncharacterized. This study aimed to identify the predictors of CD4 changes over time among HIV-infected children who began ART in Amhara, Ethiopia.

Methods

A retrospective follow-up study was performed. A total of 983 HIV-infected children who initiated ART in government hospitals in the Amhara region between 2010 and 2016 were included using a simple random sampling technique. Data were extracted using a structured checklist. An exploratory data analysis was carried out to explain individual and average profile plots. The linear mixed model was used to identify the CD4 change count predictors over time. Variables with p value < 0.05 were considered statistically significant in a multivariable linear mixed regression analysis.

Results

The mean CD4 count of the participants was 465.1 cells/mm³ with an average CD4 count increase of 30.06 cells/mm³ over 6 months from baseline CD4 count and ART initiation. Childhood age (β = − 0.015; 95% Cl − 0.021, − 0.009), opportunistic infection at ART initiation (β = − 0.044, 95% CI − 0.085, − 0.004), hemoglobin level (β = 0.013; 95% CI 0.004, 0.022), and baseline WHO clinical stage II (β = − 0.046, 95% CI − 0.091, − 0.0003) were significant predictors of CD4 changes over time.

Conclusions

The average CD4 count increase was sufficient in HIV patients who began combined antiretroviral therapy over time. The younger age of the infant, the higher baseline level of hemoglobin, the baseline WHO clinical stage II, and opportunistic infections led to changes in CD4 counts. As a result, timely diagnosis and treatment of opportunistic infections will reduce the risk of opportunistic infections.