A Formal Framework For Incorporating Equity Into Health Care Quality Measurement

Quality measurement is an important tool for incentivizing improvement in the quality of health care. Most quality measurement efforts do not explicitly target health equity. Although some measurement approaches may intend to realign incentives to focus quality improvement efforts on underserved groups, the extent to which they accomplish this goal is understudied. We posit that tying incentives to approaches on the basis of stratification or disparities may have unintended consequences or limited effects. Such approaches might not reduce existing disparities because addressing one aspect of equity may be in competition with addressing others. We propose equity weighting, a new measurement framework to advance equity on multiple fronts that addresses the shortcomings of existing approaches and explicitly calibrates incentives to align with equity goals. We use colorectal cancer screening data derived from 2017 Medicare claims to illustrate how equity weighting fixes unintended consequences in other methods and how it can be adapted to policy goals.

US Military Healthcare Professionals' Practice, Knowledge, and Misconceptions About Concussion

OBJECTIVE

To determine the US military healthcare professionals' knowledge and training preferences to improve diagnosis and management of concussion sustained in nondeployed settings.

PARTICIPANTS

US military healthcare professionals (physicians, physician assistants, and nurse practitioners) completed online surveys to investigate practices, knowledge, and attitudes about concussion diagnosis and treatment, as well as preferences on future training. There were 744 responses from active duty US military healthcare providers, all of whom had cared for at least one patient with mild traumatic brain injury (mTBI) in the previous 24 months.

RESULTS

The majority of physicians reported they were confident in their ability to evaluate a patient for a new mTBI (82.1%) and order appropriate imaging for mTBI (78.3%). Accuracy of identifying "red flag" symptoms ranged between 28.2% and 92.6%. A Likert scale from 1 ("not at all confident") to 4 ("very confident") was used to assess providers' confidence in their ability to perform services for patients with mTBI. With respect to barriers to optimal patient care, nurse practitioners consistently reported highest levels of barriers (90.8%).

CONCLUSIONS

Although US military providers regularly care for patients with concussion, many report experiencing barriers to providing care, low confidence in basic skills, and inadequate training to diagnose and manage these patients. Customized provider education based on branch of service and occupation, and broader dissemination and utilization of decision support tools or practice guidelines, and patient information tool kits could help improve concussion care.

Exploring the Association of State Policies and the Trajectories of Buprenorphine Prescriber Patient Caseloads

BACKGROUND

Increasing buprenorphine access is critical to facilitating effective opioid use disorder treatment. Buprenorphine prescriber numbers have increased substantially, but most clinicians who start prescribing buprenorphine stop within a year, and most active prescribers treat very few individuals. Little research has examined state policies' association with the evolution of buprenorphine prescribing clinicians' patient caseloads.

METHODS

Our retrospective cohort study design derived from 2006 to 2018 national pharmacy claims identifying buprenorphine prescribers and the number of patients treated monthly. We defined persistent prescribers based on results from a k-clustering approach and were characterized by clinicians who did not quickly stop prescribing and had average monthly caseloads greater than 5 patients for much of the first 6 years after their first dispensed prescription. We examined the association between persistent prescribers (dependent variable) and Medicaid coverage of buprenorphine, prior authorization requirements, and mandated counseling policies (key predictors) that were active within the first 2 years after a prescriber's first observed dispensed buprenorphine prescription. We used multivariable logistic regression analyses and entropy balancing weights to ensure better comparability of prescribers in states that did and did not implement policies.

RESULTS

Medicaid coverage of buprenorphine was associated with a smaller percentage of new prescribers becoming persistent prescribers (OR = 0.72; 95% CI = 0.53, 0.97). There was no evidence that either mandatory counseling or prior authorization was associated with the odds of a clinician being a persistent prescriber with estimated ORs equal to 0.85 (95% CI = 0.63, 1.16) and 1.13 (95% CI = 0.83, 1.55), respectively.

CONCLUSIONS

Compared to states without coverage, states with Medicaid coverage for buprenorphine had a smaller percentage of new prescribers become persistent prescribers; there was no evidence that the other state policies were associated with changes in the rate of clinicians becoming persistent prescribers. Because buprenorphine treatment is highly concentrated among a small group of clinicians, it is imperative to increase the pool of clinicians providing care to larger numbers of patients for longer periods. Greater efforts are needed to identify and support factors associated with successful persistent prescribing.

Optimizing the Role of Military Behavioral Health Technicians: A Survey of Behavioral Health Technicians and Mental Health Providers

Behavioral health technicians (BHTs), who are enlisted service members with the technical training to work alongside licensed mental health providers (MHPs), are an important part of the Military Health System (MHS) workforce. However, each service branch has different training requirements for BHTs, making it difficult to identify common qualifications across the BHT workforce and ensure that the MHS is making the best use of their skills. Building on prior RAND research that found inconsistencies in how BHTs were integrated across the force, researchers conducted what might be the largest survey to date of BHTs and MHPs. The results provide insights on BHTs' practice patterns, training and supervisory needs, and job satisfaction, as well as barriers to better integrating BHTs into clinical practice and steps that the MHS could take to optimize BHTs' contributions to the health and readiness of the force. Posing parallel sets of questions to BHTs and MHPs allowed comparisons of these groups' perspectives on these topics. The results revealed differences in views by service branch, time in practice, deployment history, and other characteristics. The researchers drew on these findings and recommendations to identify opportunities to optimize the BHT role.

Predicting Race And Ethnicity To Ensure Equitable Algorithms For Health Care Decision Making

Algorithms are currently used to assist in a wide array of health care decisions. Despite the general utility of these health care algorithms, there is growing recognition that they may lead to unintended racially discriminatory practices, raising concerns about the potential for algorithmic bias. An intuitive precaution against such bias is to remove race and ethnicity information as an input to health care algorithms, mimicking the idea of "race-blind" decisions. However, we argue that this approach is misguided. Knowledge, not ignorance, of race and ethnicity is necessary to combat algorithmic bias. When race and ethnicity are observed, many methodological approaches can be used to enforce equitable algorithmic performance. When race and ethnicity information is unavailable, which is often the case, imputing them can expand opportunities to not only identify and assess algorithmic bias but also combat it in both clinical and nonclinical settings. A valid imputation method, such as Bayesian Improved Surname Geocoding, can be applied to standard data collected by public and private payers and provider entities. We describe two applications in which imputation of race and ethnicity can help mitigate potential algorithmic biases: equitable disease screening algorithms using machine learning and equitable pay-for-performance incentives.

Association of the Healthy, Hunger-Free Kids Act of 2010 With Body Mass Trajectories of Children in Low-Income Families

IMPORTANCE

Implemented in 2012, the Healthy, Hunger-Free Kids Act of 2010 (HHFKA) increased nutritional requirements of the National School Lunch Program (NSLP) to reverse the potential role of the NSLP in childhood obesity.

OBJECTIVE

To evaluate whether associations between the free or reduced-price NSLP and body mass growth differed after implementation of the HHFKA.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data from 2 nationally representative cohorts of US kindergarteners sampled in 1998 to 1999 and 2010 to 2011 and followed up for 6 years, through grade 5, in the Early Childhood Longitudinal Study Kindergarten Class of 1998-1999 (ECLS-K:1999, in 2003-2004) and Kindergarten Class of 2010-2011 (ECLS-K:2011, in 2015-2016). In total, 5958 children were selected for analysis from low-income families eligible for the free or reduced-price NSLP (household income <185% of the federal poverty level) who attended public schools and had no missing data on free or reduced-price NSLP participation or on body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) at kindergarten or grades 1 and 5. Data were analyzed from January 1 to September 7, 2021.

EXPOSURES

Cross-cohort comparison of before vs after implementation of the HHFKA for free or reduced-price NSLP participation at kindergarten and grades 1 and 5.

MAIN OUTCOMES AND MEASURES

Body mass index difference (BMID) from obesity threshold was the difference in BMI units from the age- and sex-specific obesity thresholds (95th percentile) and is sensitive to change at high BMI. Multigroup models by cohort included weights to balance the distribution of the 2 cohorts across a wide range of covariates. A Wald test was used to assess whether associations differed between the cohorts.

RESULTS

In the final analysis, 3388 children in ECLS-K:1999 (1696 girls [50.1%]; mean [SD] age at baseline, 74.6 [4.3] months) and 2570 children in ECLS-K:2011 (1348 males [52.5%]; mean [SD] age at baseline, 73.6 [4.2] months) were included. The best fitting model for BMID change by free or reduced-price NSLP participation across the cohorts included fixed and time-varying associations. Before HHFKA implementation, grade 5 free or reduced-price NSLP participants had higher BMID, adjusted for their prior BMID trajectory, than nonparticipants (β = 0.54; 95% CI, 0.27-0.81). After HHFKA implementation, this association was attenuated (β = -0.07; 95% CI, -0.58 to 0.45), and grade 5 associations were different across cohorts (χ21 = 4.29, P = .04).

CONCLUSIONS AND RELEVANCE

In this cohort study using cross-cohort comparisons, children from low-income families who participated in the free or reduced-price NSLP had a higher likelihood of progression to high BMI that was no longer observed after HHFKA implementation. This finding suggests that the HHFKA may have attenuated the previous association of the NSLP with child obesity disparities.

Barred from employment: More than half of unemployed men in their 30s had a criminal history of arrest

We investigate what portion of the pool of unemployed men in the United States have been arrested, convicted, or incarcerated by age 35. Using the National Longitudinal Survey of Youth, 1997, we estimate 64% of unemployed men have been arrested, and 46% have been convicted. Unexpectedly, these rates vary only slightly by race and ethnicity. Further investigation of other outcomes such as marriage, education, household net worth, and earnings shows large differences between unemployed men who have a criminal history record and those who do not. One major implication of these findings is that employment services should focus more on the special challenges facing unemployed men with criminal history records. A second implication is that statistical discrimination against unemployed members of racial minority groups, to avoid hiring those with criminal histories, is both illegal and ineffective.

Buprenorphine prescriber monthly patient caseloads: An examination of 6-year trajectories

BACKGROUND

Many active buprenorphine prescribers treat few patients monthly, but little information is available regarding how prescribers' buprenorphine caseload fluctuates over time or how long it takes new prescribers to reach higher patient caseloads. We examine buprenorphine-prescribing clinicians' patient caseloads over time and explore prescriber characteristics associated with different caseload trajectories.

METHODS

Using 2006-2018 national buprenorphine pharmacy claims, we calculate monthly patient caseloads for buprenorphine prescribers for 6 years following a clinician's first filled buprenorphine prescription. We use K-means clustering to identify clusters of clinician caseload trajectories and bivariate analyses to examine prescriber and county characteristics associated with different trajectory classes.

RESULTS

We identified 42,067 buprenorphine prescribers with 3 trajectory classes. High-volume (1.4%;n = 571) whose mean monthly patient caseload increased to approximately 40 patients through the initial 20 months and stabilized at 40 or more patients; moderate-volume (9.2%;n = 3891) whose mean patient caseload increased during the initial 20 months, stabilizing at 15-20 patients; and low-volume (89.4%;n = 37,605), who typically had fewer than 5 patients monthly. Most low-volume prescribers (n = 31,470; 83.7% of all prescribers) initially treated 1-2 patients for several months, followed by no subsequent prescribing.

CONCLUSION

Almost three-quarters of buprenorphine prescribers treated no more than a few patients for several months before ceasing buprenorphine prescribing; only 10% of prescribers averaged more than 10 patients per month over the next 6 years. Efforts are needed to identify factors contributing to prescribers being willing to continue prescribing buprenorphine over time and to prescribe to more patients in order to increase access to buprenorphine treatment.

Substance Use and Mental Health Outcomes from a Text Messaging-Based Intervention for Smoking Cessation Among Young People Experiencing Homelessness

INTRODUCTION

Cigarette smoking and associated high-risk behaviors are prevalent among youth experiencing homelessness (YEH), making appropriately tailored interventions targeting smoking behavior important for this group. We pilot tested a brief text-messaging intervention (TMI) as an adjunct to standard care for YEH who smoke and found promising preliminary effects of the intervention on smoking cessation. The purpose of the present study was to test the TMI's effect on the secondary outcomes of other substance use (including use of other tobacco/nicotine devices) and mental health symptoms.

METHODS

A total of 77 participants completed the pilot randomized controlled trial, with 40 receiving the TMI (174 automated text messages plus a group smoking counseling session and provision of nicotine patches). They completed an assessment at baseline and another three months later that evaluated use of other tobacco/nicotine devices, alcohol, marijuana, and anxiety and depression symptoms.

RESULTS

We found that the TMI helped to reduce secondary substance use behaviors and mental health symptoms among the participants; mainly there were medium effects of the intervention on changes in other tobacco/nicotine use, drinking, and anxiety and depression symptoms. The intervention did not have an effect on number of marijuana use days in the past month; however, past 30-day marijuana users who received the intervention benefited by reducing the number of times they used marijuana per day.

CONCLUSIONS

In addition to helping reduce cigarette smoking, we found that a TMI for YEH was helpful in improving secondary outcomes, suggesting the promise of the TMI on benefiting YEH even beyond targeted smoking behavior.

IMPLICATIONS

This pilot study demonstrates that by targeting cigarette smoking using a text message-based intervention among youth experiencing homelessness, effects may be seen in other areas of functioning such as other substance use and mental health.

Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach

Background: In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations.Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data.Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan.Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings.Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency.

Pilot Randomized Clinical Trial of a Text Messaging-Based Intervention for Smoking Cessation Among Young People Experiencing Homelessness

INTRODUCTION

Smoking rates are alarmingly high among young people experiencing homelessness (YEH), yet there are no evidence-based cessation programs for this population. This paper presents results from a pilot evaluation of a text messaging-based smoking cessation treatment, as an adjunct to brief group cessation counseling, to improve abstinence rates among 18-25 year old smokers experiencing homelessness. The goal of this study was to estimate effect sizes for a larger trial and it was not powered to detect group differences.

METHODS

YEH smokers who had a working cell phone with them at recruitment were randomized to receive a group counseling session, nicotine patches, and written material on quitting (n=37) or a similar program that also included a 6-week automated text messaging intervention (TMI) to provide ongoing support for quitting (n=40). Smoking outcomes were evaluated through a 90-day follow-up.

RESULTS

7-day point prevalence abstinence at 90-day follow-up was higher in the TMI condition than standard condition (17.50% vs. 8.11%, respectively; Cohen's h=.37); however, the 90-day continuous abstinence rate was not statistically different from zero in either condition. Reductions in the number of days smoked in the past 30 days from baseline to follow-up were greater in the TMI condition than the standard condition (-14.24 vs. -8.62, respectively; Cohen's d=.49).

CONCLUSIONS

Adding a 6-week TMI support to a brief group counseling and pharmacotherapy protocol holds promise for smoking reduction and abstinence among YEH smokers. Results indicate that further development and evaluation of the TMI in this population is warranted.

IMPLICATIONS

This is the first study to evaluate the feasibility of using a text messaging-based intervention (TMI) for behavior change with 18-25 year olds experiencing homelessness, and more specifically, the first to test a TMI to provide ongoing support for smoking cessation. Small to medium effect sizes for the TMI are promising in terms of implementing a TMI using participants' own cell phones, as well as the efficacy of this approach as an adjunct to standard care (brief group counseling and pharmacotherapy) for smoking cessation among young people experiencing homelessness.

Linking Statistics With Testing Policy to Manage COVID-19 in the Community

OBJECTIVES

To determine the public health surveillance severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing volume needed, both for acute infection and seroprevalence.

METHODS

Required testing volumes were developed using standard statistical methods based on test analytical performance, disease prevalence, desired precision, and population size.

RESULTS

Widespread testing for individual health management cannot address surveillance needs. The number of people who must be sampled for public health surveillance and decision making, although not trivial, is potentially in the thousands for any given population or subpopulation, not millions.

CONCLUSIONS

While the contributions of diagnostic testing for SARS-CoV-2 have received considerable attention, concerns abound regarding the availability of sufficient testing capacity to meet demand. Different testing goals require different numbers of tests and different testing strategies; testing strategies for national or local disease surveillance, including monitoring of prevalence, receive less attention. Our clinical laboratory and diagnostic infrastructure are capable of incorporating required volumes for many local, regional, and national public health surveillance studies into their current and projected testing capacity. However, testing for surveillance requires careful design and randomization to provide meaningful insights.

A Likelihood-Free Estimator of Population Structure Bridging Admixture Models and Principal Components Analysis

We introduce a simple and computationally efficient method for fitting the admixture model of genetic population structure, called ALStructure The strategy of ALStructure is to first estimate the low-dimensional linear subspace of the population admixture components, and then search for a model within this subspace that is consistent with the admixture model's natural probabilistic constraints. Central to this strategy is the observation that all models belonging to this constrained space of solutions are risk-minimizing and have equal likelihood, rendering any additional optimization unnecessary. The low-dimensional linear subspace is estimated through a recently introduced principal components analysis method that is appropriate for genotype data, thereby providing a solution that has both principal components and probabilistic admixture interpretations. Our approach differs fundamentally from other existing methods for estimating admixture, which aim to fit the admixture model directly by searching for parameters that maximize the likelihood function or the posterior probability. We observe that ALStructure typically outperforms existing methods both in accuracy and computational speed under a wide array of simulated and real human genotype datasets. Throughout this work, we emphasize that the admixture model is a special case of a much broader class of models for which algorithms similar to ALStructure may be successfully employed.