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We interview experts on investigational treatments such as liso-cel, and ask how to bring them to community practice.

Spotlight on CAR T-Cell Therapy

This AJMC® Profiles in Care provides an in-depth look into the results of the outcomes-based agreement between Boehringer Ingelheim and Highmark Inc.

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If 2020 was dominated by the news of how COVID-19 spread across the globe, then 2021 has so far been focused on ending the pandemic through vaccine distribution.

A Timeline of COVID-19 Vaccine Developments in 2021

View news from the ERS International Congress 2021, including session summaries, poster write-ups, and interviews with key opinion leaders

Coverage of European Respiratory Society International Congress 2021

A new report suggests long-term use of Benlysta (belimumab) is safe in patients with systemic 

 erythematosus (SLE) and can lead to durable responses similar to those in other populations.

, offers some of the first long-term safety and efficacy data among patients in East Asia and shows that the therapy can reduce reliance on corticosteroids.

The authors noted that SLE is more common, and generally more severe, in non-White populations than in White populations.

“​​In Japan, SLE is estimated to affect 4.3 to 37.7 people per 100,000, and in South Korea, the estimated prevalence is 20.6 to 26.5 people per 100,000,” they wrote.

The treatment landscape for SLE has gaps. Although patients are generally prescribed immunosuppressants and corticosteroids, many patients still experience a significant disease burden and can suffer adverse effects (AEs) from long-term use of those drugs.

Belimumab is a recombinant humanized immunoglobulin G1 lambda monoclonal antibody that binds to and antagonizes the biology of B-lymphocyte stimulator protein. Patients with SLE have elevated levels of this protein, the investigators wrote.

Intravenous belimumab is approved in the United States, Europe, Japan, and South Korea, yet the authors said there is little in the way of long-term safety and efficacy evidence in Japanese and South Korean populations, despite higher prevalence of the disease in those populations.

They constructed a phase 3 open-label continuation study that built upon 2 previous studies of belimumab. Patients who completed the earlier studies were given 10 mg/kg of belimumab once every 4 weeks for up to 7 years. The study’s primary end point was safety, and the secondary end points were response rate, SLE flares, and prednisone use.

A total of 142 patients were enrolled, all of whom had moderate SLE; 73.2% of patients completed the study. The patients in the study sought care from one of 27 study centers in Japan and South Korea.

In terms of safety, most of the patients (97.4%) experienced AEs, although only about one-third (33.8%) were considered serious AEs. Cellulitis, contusion, and herpes zoster were the most commonly reported serious AEs. Only 4.9% of patients experienced an AE that led to withdrawal from the study.

“No trends of clinical concern were observed over time with regard to incidence of grade 3 or 4 values in hematology parameters, clinical chemistry, and urinary values,” the authors wrote. “There were no grade 3 or 4 serum IgG values reported post first dose of belimumab.”

Turning to the secondary end points, disease activity index scores suggested a significant reduction in disease activity in many patients over the life of the study. For instance, the number of patients achieving the benchmark of 

“Overall, the proportion of SRI4 responders during belimumab treatment increased from 47.8% (65 of 136 patients) at Year 1, Week 24, to 68.2% (15 of 22 patients) at Year 6, Week 48,” the investigators said.

Severe flares occurred in 14.8% of patients, at a median of 585 days following the first dose of belimumab. However, the data also showed that patients’ use of prednisone decreased over the long term, a positive sign for belimumab’s efficacy.

“In conclusion, the safety results of this 7-year study of patients with SLE from Japan and South Korea are consistent with those observed in previous 7- [to] 13-year open-label continuation studies of belimumab added to standard therapy,” the authors wrote.

Tanaka Y, Bae S-C, Bass D, et al. Long-term open-label continuation study of the safety and efficacy of belimumab for up to 7 years in patients with systemic lupus erythematosus from Japan and South Korea. 

. Published online July 7, 2021. doi:10.1136/rmdopen-2021-001629

Articles

This new study adds to the literature by giving the first long-term data in these populations.

Belimumab Has Favorable Safety, Efficacy in Patients With SLE

 since 2019 and has a BA in philosophy and journalism & professional writing from The College of New Jersey. 

When we have the benefit of preventing a portion of those screened for type 1 diabetes from developing clinical disease, then economically, widespread screening will be much more accepted, said Ezio Bonifacio, PhD, a professor of diabetes and preclinical stem cell work at the Technical University of Dresden, in Germany.

What barriers inhibit widespread screening for type 1 diabetes (T1D) in children? What steps can be taken to overcome these barriers?

I'm part of 2 studies to do widespread screening, so genetically we can do it, at least in Europe. We have a program that genetically screens, we've screened nearly 300,000 newborns in this program, the 

 [The Global Platform for the Prevention of Autoimmune Diabetes] one, that 

. That's genetic screening. In Germany, we have an antibody screening for children. That's done well over 100,000. It's possible to be done.


As always, the barriers are: for now, these remain research studies. You've got to convince authorities that there's a benefit, that is financial, and health. The health one is, there are some health benefits. We identify them, these children, we can look after them, prevent diabetic ketoacidosis when they develop diabetes, etc. But the key benefit is the prevention. We need to have some way to prevent a portion of them developing clinical disease.


Second, as I said, economical. When we have the clinical benefit of eliminating some clinical cases, then economically it will be much more accepted. And those who have to pay for that screening, governments or whoever it is in the country will be more likely to do that.


But just one thing to add on that, in terms of finding those interventions, we're really good at screening children. But most of the interventions are not quite there yet to test in children. It would be great to have interventions that we can give to 3-year-old children, at least also in trial. And not all the ones that are out there to test are yet available for that age.

Would you recommend doing genetic screening first and then testing those at risk for antibodies, or would you screen everybody for antibodies?

There is controversy on whether it's more practical to select people for antibodies testing based on their genetic risk, or to test everybody for antibodies. Our data suggests that it's going to be much more effective to test everybody for antibodies. There are really cheap ways to do that now. You can have cheap screening antibody tests, which then select out those few who need further antibody tests.


With a genetic test up front, you're going to exclude a lot of people who are going to get diabetes, more than 50% and probably 60%-70%. Therefore, you've already eliminated a possibility of treating with something that works in the majority of people. By testing antibodies, the trick is to find the right ages to screen, because some children develop it early, and then get diabetes. So if you screen too late, you've missed them. And some people will develop it a little bit later.


But what we found is that many of the children who develop their autoantibodies or diabetes, let's say, by the time they're adolescents, they already have their antibodies somewhere in that first 5, 6 years of life. If you screen around 3 years of age, you have a reasonably good balance. You're probably get to pick up 50% of those that develop T1D by a single antibody screening. I think it's cost effective to do the antibody testing. And in the long run, it's going to be the one that's going to detect most cases. That's from our data in Europe. And of course, it may be a little bit different in other countries and particularly other ethnic groups where, 

Videos

Ezio Bonifacio, PhD, addresses barriers inhibiting widespread screening for type 1 diabetes (T!D), and offers insights on whether all individuals should be screened, or only those known to be at genetic risk for the disease. 

Dr Ezio Bonifacio Discusses Barriers to Widespread T1D Screening 

US Male Life Expectancy Drops by 2.2 Years

The COVID-19 pandemic reduced life expectancy in 2020 by the largest amount since World War II, as 

. According to a study published by Oxford University, men’s life expectancy decreased more than women’s in most countries, with American men seeing the biggest drop of 2.2 years compared with 2019. Of 29 countries studied, 27 of them saw a reduction in life expectancy and 22 saw a decrease by more than 6 months compared with 2019.

Pfizer/BioNTech to Seek Authorization of Vaccine for Children Under 12

Pfizer/BioNTech plan to request authorization of their COVID-19 vaccine for children younger than 12 years, 

. CDC director Rochelle Walensky, MD, MPH, said once Pfizer submits the data on vaccines for children aged 5 to 11 years, both the FDA and CDC will review it with urgency and hopefully authorize the vaccine within weeks. Until then, the CDC recommends that children wear masks and socially distance to prevent the spread of COVID-19, especially in school settings.

School Mask Mandates Reduce Spread of COVID-19 in Children

The CDC released more evidence that school mask requirements are associated with lower spikes in pediatric COVID-19 cases and fewer school outbreaks, 

. After analyzing data from 520 US counties, the CDC found that COVID-19 cases in children rose more sharply in schools without mask requirements. After looking at 2 of Arizona’s most populous counties, the agency also learned that schools without mask requirements were 3.5 times more likely to have an outbreak compared with schools with mask requirements. Since the school year started, more than 900,000 students in 44 states have already been affected by closures.

Twenty-two countries saw a 6-month reduction in life expectancy; Pfizer/BioNTech plan to request authorization of a COVID-19 vaccine for children aged 5 to 11 years; schools with mask requirements had fewer COVID-19 outbreaks.

What We’re Reading: COVID-19 Reduced Life Expectancy; Pfizer to Seek Vaccine Authorization in Children; School Mask Requirements

Results of a nationally representative cohort study revealed Medicaid expansion was associated with better 5-year health performance outcomes for federally qualified health centers (FQHCs). In particular, data showed FQHCs in Medicaid-expanded states experienced improved blood pressure and glucose control measures over 5 years for Black and Hispanic patients when compared with data from FQHCs in nonexpansion states. Findings were published in 

FQHCs provide primary care services to almost 30 million low-income Americans without concern for ability to pay, and typically serve 1 in 5 Medicaid enrollees, 1 in 5 rural residents, and 1 in 3 individuals with an income lower than the federal poverty level (FPL), authors explained.

“In 2013, before implementation of Medicaid expansion under the US Affordable Care Act (ACA), 44% of adults who were utilizing FQHCs did not have insurance, 92% had incomes less than 200% of the FPL, and 65% were members of racial and ethnic minority groups,” they added.

Although previous research has showed Medicaid expansion was associated with improved quality of care and increased service capacity at FQHCs in the short term, limited evidence exists regarding longer-term associations of expansion with FQHCs across the country.

To address this knowledge gap, investigators examined 5-year changes in uninsurance, blood pressure, and glucose control following Medicaid expansion using a representative sample of all FQHCs.

As uninsured and underinsured individuals with hypertension and 

 often forgo effective treatment that can improve outcomes, and because these 2 conditions disproportionately affect some racial and ethnic minority groups, researchers focused their analyses of FQHC-level performance on blood pressure and glucose control data.

Publicly available information was gleaned from the Health Resources and Services Administration 2012-2018 Uniform Data System. To measure 5-year outcomes, only FQHCs in states that had expanded Medicaid by the end of 2014 were included in analyses.

“A total of 578 FQHCs (61.1%) were in Medicaid expansion states, representing 13 million patients per year at baseline, and 368 FQHCs (38.9%) were in nonexpansion states, representing 6 million patients per year at baseline,” researchers wrote.

Using difference-in-differences analyses, investigators assessed data in expansion and nonexpansion states before (2012-2013) vs after (2014-2018) expansion. Of the patients served at all 946 FQHCs, 64.4% were aged between 18 and 64 years, 57.4% were women, 18.9% were non-Hispanic Black, and 27.3% were Hispanic.

Following expansion, FQHCs in Medicaid expansion states experienced a 9.24 percentage point (PP) (95% CI, 7.94-10.54) decline in rates of uninsurance over the 5-year period compared with non–expansion-state FQHCs.

Over 5 years, expansion was associated with a 1.61-PP (95% CI, 0.58-2.64) comparative improvement in hypertension control and a 1.84-PP (95% CI, 0.71-2.98) comparative improvement in glucose control.

Stratified results suggest that improvements were consistently observed in Black and Hispanic populations.

Magnitude of change tended to increase with implementation time, as by year 5, expansion was associated with a 3.38-PP (95% CI, 0.80-5.96) comparative improvement in hypertension control and a 3.88-PP (95% CI, 0.86-6.90) comparative improvement in glucose control among Black populations.

A 2-PP increase in hypertension control across all FQHCs equates to nearly 100,000 additional patients with hypertension at FQHCs with blood pressure control.

“We observed larger associations between expansion and health outcome measures in the long vs short term following expansion, suggesting that intermediate outcomes, such as blood pressure and glucose control, may take time to improve following coverage expansions,” authors noted.

In addition, as expansion may also improve the financial revenue of FQHCs, these centers may be able to expand capacity or invest in quality improvement interventions.

Because previous research also showed that racial disparities in blood pressure control and hypertension among FQHCs patients persist, researchers stressed insurance alone will not eliminate these disparities. “Black, Hispanic, and American Indian/Alaskan Native patients face systemic inequities and structural racism that make it especially difficult to control blood pressure and glucose levels,” they said.

Authors were unable to adjust for individual-level factors, meaning they were unable to distinguish how gains in insurance coverage for patients vs gains in patient revenue for FQHCs were associated with outcomes, marking a limitation to the study.

“These findings suggest that over the long term, expanding Medicaid in all states may improve chronic disease health outcome measures at FQHCs, which provide care to millions of underserved patients,” they concluded.

Cole MB, Kim J, Levengood TW, and Trivedi AN. Association of Medicaid expansion with 5-year changes in hypertension and diabetes outcomes at federally qualified health centers. 

. Published online September 10, 2021. doi:10.1001/jamahealthforum.2021.2375

Results of a representative study of patients at federally qualified health centers found Medicaid expansion was associated with reduced rates of uninsurance, improved blood pressure and diabetes control measures, and progress in closing racial care disparities over 5 years.

Medicaid Expansion Linked With Reduction in Uninsurance and Improved Blood Pressure, Glucose Control Measures

About 1 in 5 people admitted to the hospital for 

) in the United States end up back in the hospital within 1 month, and those who do face a higher risk of mortality, according to a new study.

This new report is based on nationwide readmission data from 2017 and was published in 

International Journal of Colorectal Disease

The authors noted that about 15% of cases of health care–related infections are tied to CDI, and yet data about CDI readmission remain limited. Studies that do exist tend to be small in scope, be based on a single center, or have methodological problems, they wrote.

Explaining that they wanted to see what they could learn about CDI by using a large, nationally representative database of hospital readmissions, the investigators decided to consult the National Readmission Database, the largest data set of its kind. The database contains reports from nearly 2500 hospitals in 28 states, and the data are de-identified to protect patient privacy.

International Classification of Diseases, 10th Revision, Clinical Modification 

codes to find patients admitted to the hospital for CDI. Those patients were then tracked for 30- and 90-day readmissions. In addition to those primary outcomes, the secondary outcomes were mortality rates and health care consumption.

The authors found 83,865 patients who were discharged from the hospital after being hospitalized with a primary diagnosis of CDI. Within 30 days of discharge, 22.37% of patients were back in the hospital, and by the 90-day mark, another 15.01% had been readmitted. In 30% of those cases, the cause of readmission was recurrent CDI.

The authors found certain factors decreased a patient’s likelihood of readmission.

“Age greater than 60 years and private insurance were found to be protective factors for readmission at both 30 and 90 days, with female gender being an additional protective factor for readmission at 30 days,” they wrote.

The fact that older patients had a smaller chance of being readmitted is likely not related to biological factors, the investigators said, but rather because older patients are more likely to receive home health care. The lower readmission rate could also be caused by patients dying outside of the hospital, an event that would not be reflected in these data.

When patients were readmitted, the authors found they had a greater risk of dying in the hospital and were more likely to have higher health care costs. For instance, during the initial hospitalization, the mortality rate was just 1.41%; however, for those readmitted within 30 days, the mortality rate was 4.86%, and within 90 days, 4.40%. Readmitted patients also had longer hospital stays and higher health care charges.

The investigators concluded that physicians must be vigilant with patients admitted to the hospital for CDI in order to reduce the likelihood of readmission and of mortality.

“[W]e would suggest more expedient care for those hospitalized for recurrent CDI based upon this finding,” they wrote, adding that particular attention should be paid to patients at higher risk of readmission. “This information may facilitate the recognition of high-risk patients, optimize the distribution of resources, and ultimately result in lower mortality and health care consumption among patients with CDI.”

Sharma S, Weissman S, Walradt T, et al. Readmission, healthcare consumption, and mortality in Clostridioides difficile infection hospitalizations: a nationwide cohort study. 

. Published online August 7, 2021. doi:10.1007/s00384-021-04001-w

When patients are quickly readmitted, they have higher health care costs, a new study has found.

Readmission Common Following CDI Hospitalization, Study Finds

 Existing literature indicates that multimorbidity, mental health (MH) conditions, substance use disorders (SUDs), and social determinants of health are hallmarks of high-need, high-cost patients. Health Resources and Services Administration–funded health centers (HCs) provide care to nearly 30 million patients, but data on their patients’ complexity and utilization patterns are limited. We identified subgroups of HC patients based on latent concepts of complexity and utilization.

We used cross-sectional national data from the 2014 Health Center Patient Survey and latent class analyses to identify distinct and homogenous groups of complex high-utilizing patients aged 18 to 64 years.

We included indicators of chronic conditions (CCs), MH, SUD risk, and health behavior to measure complexity. We used number of outpatient and emergency department visits in the past year to measure utilization.

 HC patients were separated in 9 distinct groups based on 3 complexity latent classes (MH, multiple CCs, and low risk) and 3 utilization classes (low, high, and superutilizers). Conditions associated with each subgroup differed. The highest prevalence of bipolar disorder (45%) and high SUD risk (6%) was observed among MH superutilizers, whereas the highest prevalence of cardiovascular disease (48%) and obesity (96%) was seen among CC superutilizers. Most MH superutilizer patients concurrently had MH conditions and obesity and were smokers, but most CC superutilizer patients concurrently had hypertension, obesity, and cardiovascular disease.

 Our examination of complexity and utilization indicated distinct HC patient populations. Managing the care of each group may require different targeted intervention approaches such as multidisciplinary care teams that include MH providers or specialists.

We identified 9 latent subgroups that represented the health status and utilization profiles of complex patients who use Health Resources and Services Administration–funded health centers. The findings demonstrate the importance of understanding health behaviors, physical and mental health conditions, and utilization patterns to develop effective interventions for complex, high-utilizing patients.

Three complexity classes (mental health, multiple chronic conditions, low risk) and 3 utilization classes (low, high, and super-utilizers) were identified.

The intersection of these categories provided insight on the highest prevalence of characteristics and co-occurring conditions among latent subgroups.

Interventions and treatments must consider these patients holistically and incorporate care coordination among providers for the provision of patient-centered care.

small proportion of individuals incur the majority of health care spending in the United States.

 The quest to reduce and contain the escalating costs of care in the United States has generated extensive research on identifying high-cost patients, the reasons for their high costs, and what can be done to reduce those costs. A significant number of studies have focused on high utilization of services, categorizing patients based on frequent emergency department (ED) visits and hospitalizations.

 Research aimed at distinguishing characteristics of high utilizers (HUs) is dominated by assessing complicating factors such as mental health (MH) conditions, substance use disorders (SUDs), and homelessness.

 This has led to using labels such as “high-need high-cost” and calls for effectively managing these patients’ needs to reduce costs and improve their health.

 Most research on these patients is focused on populations with specific forms of coverage and patients of specific health care providers that grapple with identifying ways to address their patients’ needs and reduce utilization and costs. For example, some studies have focused on specific populations of payers, such as Medicaid, private insurance, or Medicare, or patients of integrated delivery systems or specific hospitals.

Information that characterizes complex HUs is limited for nearly 30 million low-income and uninsured patients served by Health Resources and Services Administration (HRSA)–funded health centers (HCs), also called community or federally qualified HCs.

 HCs may be funded under Community HC, Migrant HC, Health Care for the Homeless, or Public Housing Primary Care programs.

HCs are the cornerstone of the safety-net system of care in the United States and provide comprehensive and culturally competent primary care and social services regardless of the ability to pay at more than 13,000 delivery sites.

 Evidence shows that HC patients have lower rates of ED visits and hospitalizations compared with patients served at other primary care providers.

 Yet, HCs serve patients with a high burden of disease, including diabetes (15%), hypertension (28%), asthma (6%), overweight/obesity (23%), depression (50%), generalized anxiety (35%), and panic disorders (19%).

 Additionally, these conditions are complicated by high rates of high-risk behaviors such as smoking and SUDs.

 This high burden of disease is further complicated by the disadvantages of being low income and uninsured and is likely a barrier to improving outcomes of care.

 Thus, successful provision of effective primary care in HCs has a crucial role in the overall success of efforts to improve population health and reduce costs.

There is wide variability in defining complexity in the literature, making it difficult to identify and treat such patients.

 Complex patients are frequently distinguished by presence of multiple chronic conditions (CCs), complicating MH diagnosis and SUDs, risky behaviors, social determinants of health (SDOH) such as unstable housing and employment, and environmental factors.

 Conceptualization of complexity has varied by health care discipline, and various conceptual frameworks have been developed to identify and describe complexity and quantify the extent of the relationship among complexity aspects.

Joint identification of complex patients and HUs can be instrumental in effective treatment of these patients, which includes delivery of a coordinated combination of medical, MH, and social interventions.

 This moves away from focusing on a specific condition or disease or treating complex patients as homogenous groups.

 A deeper understanding is needed of the confluence of factors that lead to complexity and of the subsequent relationship of complexity with high utilization of services.

In this context, we aimed to simultaneously characterize complexity and high utilization among HC patients aged 18 to 64 years by identifying distinct subgroups. We described each population by examining the prevalence of specific conditions within each group. We also identified co-occurrence of various indicators of complexity and HUs for patients within each group. This information would allow for a better understanding of which subgroups of HC patients need targeted and focused interventions.

We used the 2014 Health Center Patient Survey, a nationally representative cross-sectional survey of HC patients that included information on patient demographics, health care utilization, health conditions, and behaviors. The 3-stage sampling design included the selection of 169 HC organizations, 520 HC sites within those organizations, and a random sample of HC patients who were eligible if they had visited the HC once in the past 12 months and were interviewed during their second appointment. A total of 7002 patient interviews were surveyed, representing 59.1% of all patients screened. Patients were excluded if they refused, discontinued the screening interview, or did not have a prior visit. The survey completion rate was 91.4%. We restricted our sample to patients aged 18 to 64 years to reduce significant variations in health conditions, utilization patterns, and insurance coverage between these adults and children or older adults.

 Our final analytic sample size totaled 5040.

We included 2 sets of variables to measure the latent concepts of complexity and high utilization. To measure complexity, we included health indicators, MH, SUD, and health behaviors guided by the literature.

 We used direct, objective health indicators including having hypertension, diabetes, asthma, cancer, stroke, chronic obstructive pulmonary disease (COPD), liver disease, weak/failing kidneys, and cardiovascular disease (CVD). We also included indicators of overweight/obesity status. Indicators of MH included depression, generalized anxiety, panic disorder, schizophrenia, and bipolar disorder. We also included an indicator of high risk of SUD based on the World Health Organization Alcohol, Smoking, and Substance Involvement Screening Test algorithm. We used current or former smoking (vs never) as a health behavior indicator.

To measure HUs, we used self-reported number of ED and outpatient visits in the past year. To help understand unique characteristics of latent classes, we examined demographics such as age, gender, race/ethnicity, education, and employment. We also examined health insurance and poverty. In addition, we examined subjective or less direct health indicators including self-assessed health status, any functional limitations (any instrumental activities of daily living), homelessness, and veteran status. Further detail on the constructions of these measures can be found in 

We used latent class analysis (LCA) to simultaneously identify latent concepts of complexity and high utilization.

 LCA is an established methodology used to define subgroups of a population based on an investigator-selected list of observed characteristics and relates them to an unobserved latent variable indicated by class membership using structural equation modeling techniques (see LCA section of the 

). The results identify mutually exclusive and parsimonious and interpretable classes of individuals who share some similarities but focus on the most notable attributes of each class. The model-generated highest posterior probabilities are used to attribute each individual to a class. This approach is preferred to conventional regression approaches.

We conducted the LCA using Mplus 7 (Muthén & Muthén) and used weights to account for the survey sampling design. We identified the classes for each concept by fitting models with 2 to 4 classes and determined the best fit by assessing likelihood ratio (lower values indicate better fit), Akaike and Bayesian information criteria (lower values indicate better distinction between classes), and entropy statistic (higher values indicate accurate class identification given the number of classes).

 We took into account the sample size of each class and did not retain those with less than 3% of the sample. We included age and gender as primary confounders of complexity and health insurance status as the primary confounder of high utilization.

We described the resulting classes in Stata 15 (StataCorp) using demographics, health status, and utilization variables. We also used Gephi version 0.9.2 (Association Gephi) to visualize the interrelationships among various indicators within a class. 

 shows the prevalence of conditions (bubbles) and the co-occurrence of conditions (lines) where bigger bubbles show higher prevalence and thicker lines show a higher rate of co-occurrence. The visualization of the line thickness indicates the independent co-occurrence between conditions. We calculated the thickness of the lines by summing the sampling weights of dyads of conditions among all patients with these conditions and rescaling them from 0 to 1 to visually present the relationship between dyads.

Sample characteristics showed high rates of hypertension (39%), diabetes (20%), obesity (50%), depression (44%), anxiety (30%), and current or past smoking (47%) (

). Additional health status indicators showed many with fair/poor health (42%) and functional limitations (38%). The majority were Medicaid beneficiaries (54%), and many were not in the labor force (42%) or were employed (40%). Patients had a mean of 8.3 outpatient and 1.6 ED visits last year.

The LCA led to the identification of 3-class complexity and 3-class high utilization latent variables based on best fit and parsimony (

 shows an MH conditions class (32%) dominated by highest prevalence of conditions such as depression (92%), general anxiety (85%), panic disorder (48%), bipolar disorder (39%), and schizophrenia (10%). This class also had the highest prevalence of smoking (70%). A second multiple CC class (27%) was dominated by highest prevalence of hypertension (80%), obesity (71%), diabetes (46%), and asthma (14%). The third class was the largest (41%) and dominated by lowest prevalence of all the examined conditions and was classified as low risk (LR). These individuals may still be at risk for developing more severe and complex conditions due to dominance of risk factors such as smoking, obesity, CVD, and depression.

The prevalence of indicators for the 3 classes of high utilization indicated low utilizers (LUs; 73%), HUs (24%), and superutilizers (SUs; 3%) (

). Among LUs, the mean numbers of outpatient and ED visits last year were 3.8 and 1.2, respectively. HUs represented 24% of the sample and had a mean of 15.3 outpatient and 2.3 ED visits last year. SUs (3%) had a mean of 56.7 outpatient and 6.0 ED visits.

Comparing the SUs across the complexity classes shows that those in the MH class had the highest prevalence of depression (95%), bipolar disorder (45%), COPD (25%), cancer (14%), stroke (10%), and high SUD risk (6%) (

). Among the CC class, SUs had the highest rates of obesity (96%), being current or past smokers (83%), and CVD (48%). Among the LR class, SUs had high rates of being current or past smokers (66%), obesity (62%), depression (52%), and overweight (33%).

Comparing HUs, the MH class had the highest rates of general anxiety (88%) and asthma (30%); the CC class had the highest prevalence of hypertension (89%), liver conditions (15%), and weak/failing kidneys (10%); the LR class had the highest rate of overweight but was not distinguishable by high prevalence of specific chronic or MH conditions (

). Additional data on LU characteristics in each complexity class are available in 

Using the underlying LCA data, we displayed the co-occurrence of combinations of most prevalent conditions for individual patients in each group (eAppendix Figure 1). Within the MH SUs, most patients had MH conditions along with hypertension, were obese or overweight, and were current or past smokers. In contrast, within the small CC SU group, most patients had hypertension and CVD, were current or past smokers, and were overweight or obese. Within the small group of LR SUs, most patients were overweight or obese, but few had CCs. Complete prevalence of complex conditions, sociodemographic, financial, and health status indicators among the 9 latent classes are available in 

Using a nationally representative sample of patients who obtain care services at HRSA-funded HCs, we identified 3 complexity and 3 high utilization latent classes among patients aged 18 to 64 years. The complexity classes indicated distinct populations of patients with a high prevalence of MH conditions, multiple CCs, or conditions and behaviors that might increase the risk of more complex conditions. The high utilization classes also identified distinct populations separated by gradation in use of outpatient and ED visits from low to high to super-HUs. The confluence of 3 latent classes identified 9 distinct classes with different profiles of health and utilization and common co-occurrence of specific chronic or MH conditions.

Our classification of complex patients in this study is consistent with other studies that highlight the high rate of co-occurrence of MH conditions, but we also show a distinct separation between those who are primarily defined by high prevalence of MH conditions with some CCs vs those primarily defined by CCs complicated with depression.

 Our findings indicating co-occurrence of MH conditions with smoking status or of hypertension and diabetes with obesity have been shown in the literature, but other relationships such as high prevalence of MH conditions with obesity or hypertension are more emergent.

 Our utilization classes found that only 3% are SUs, supporting the existing literature that only a small proportion of the population are SUs.

 Of those characterized as SUs, our findings support previous contributions that the majority are driven by MH conditions, followed by CCs such as cancer or social risk factors.

 Our classification of patients with high utilization is also consistent with studies that distinguish SUs as those with many ED visits, but we examined the combined use of outpatient and ED visits and separated SUs from other HUs.

 Our joint classification of patients by complexity and high utilization is aligned with the concept of high-need, high-cost patients and provides insights into 9 homogenous and distinct groups of HC patients who require targeted interventions designed to address their unique health care needs.

Our findings have implications based on complexity alone, as well as for complex patients who are HUs or SUs. The MH class is characterized clinically by a high prevalence of MH conditions along with other CVD risk factors including smoking, obesity, and hypertension. These findings suggest that these risk factors cannot be treated effectively without addressing MH issues, and, therefore, collaboration between primary care providers and psychiatrists and other MH specialists is most appropriate to treat this patient population.

 The CC class is also characterized by CVD risk factors including hypertension, obesity, and diabetes, and it has lower prevalence of most MH conditions. These risk factors differ somewhat from those of the MH class. This patient population would likely benefit from appropriate interventions to reduce cardiovascular events, including lifestyle modification and medications to control blood pressure and blood glucose level. The LR class is not dominated by chronic health or MH conditions but likely reflects those with risk of developing more severe and complex health conditions, such as diabetes or hypertension, based on their risk factors including obesity, smoking behavior, and depression. These patients would probably benefit most from intensive patient-centered primary and preventive care to arrest development of more serious conditions.

By focusing on complexity classes and SUs, more refined sets of interventions to avoid excessive service use emerge. SUs with MH complexity may require the presence of MH providers to manage patients’ depression and bipolar disorder; SUD staff in the care team to address high SUD risk; and significant coordination with oncologists, pulmonary specialists, and neurologists.

 In contrast, reducing excessive use among SUs with CCs may require a greater focus on smoking cessation efforts in the primary care setting, including nutritionists in care teams and coordination with cardiologists.

 Similarly, care of HU patients with CCs may require a greater focus on managing patients with hypertension and coordination with hepatologists and nephrologists to reduce avoidable use. Additionally, care of HU patients with MH complexity may require better management of general anxiety and asthma to reduce avoidable care use. Further research is required to better assess factors associated with high use of care by LR patients.

Our findings highlight the importance of providing effective care to HC patients and the potential complexity of this task, which is complicated by SDOH such as low income, lack of transportation, inability to take time off from jobs that do not offer paid time for medical care, and lack of insurance. The profile of these patients indicates the need for multidimensional approaches including multidisciplinary teams of providers and care coordination with specialists and other providers outside the HC. In addition, treatment of these patients requires multiple care steps and visits. These issues have led to HRSA initiatives to help maximize patients’ access to integrated care and promoting the patient-centered medical home model of care. HRSA has also made significant investments in behavioral health and primary care integration, including establishing a supplemental funding opportunity in 2019 to help increase behavioral health staffing and access in HCs.

 Our data indicate the importance of escalating these and other efforts to improve the health of these patients.

We used survey data for this study, which may be susceptible to measurement error and recall bias. In particular, utilization indicators are likely to be estimates at the higher ranges. The sample sizes of SUs among LR and CC classes were small. We lacked other important indicators of high utilization such as hospitalizations and expenditures. The sample was not large enough to test the fit of additional complexity and high-utilization classes and did not allow us to include other indicators of complexity such as severity. The number of conditions identified in the data were also not exhaustive of all diagnoses for each patient. In addition, our measure of complexity lacked the provider perspective, which would include as assessment of the severity of conditions and other observations of patients’ problems.

 We also lacked a more direct measure of actual SUD and relied on risk status. We lacked data on SDOH, receipt of social support services, and ability of patients to manage their condition, which are important indicators for utilization and health status.

 We lacked data on future utilization patterns to assess regression to the mean.

Our findings are primarily generalizable to patients of HCs, who may differ from low-income and uninsured patients receiving their care in other settings such as private providers or hospital-based outpatient care. Our study is applicable to adult patients aged 18 to 64 years and may not be generalizable to older adults, whose condition profiles are frequently dominated by low functional status and frailty, multiple CCs, and cognitive impairment. Older adults also are primarily covered by Medicare, and these differences lead to different patterns of care utilization and access.

Our data provide a deeper understanding of the combined latent concepts of complexity and high utilization for nearly 30 million low-income and uninsured patients receiving care from HRSA-funded HCs.

 Our findings provide insights into factors associated with complexity and high utilization and how the care of these patients may be adequately or effectively managed. Improvement in health of these patients could reduce unnecessary outpatient and ED visits and garner savings at the federal level.

 University of California, Los Angeles (UCLA) Center for Health Policy Research (NP, XC, CL, WZ), Los Angeles, CA; Department of Health Policy and Management, UCLA Fielding School of Public Health (NP, YT), Los Angeles, CA; Division of General Internal Medicine and Health Services Research, UCLA Geffen School of Medicine (YT), Los Angeles, CA; Office of Quality Improvement, Bureau of Primary Health Care, Health Resources and Services Administration (HH, BH, JB, AS), Rockville, MD.

 This article was funded by the US Department of Health and Human Services (HHS), Health Resources and Services Administration (HRSA), under contract number HHSH250201300023I. The views expressed in this publication are solely the opinions of the authors and do not necessarily reflect the official policies of HHS or HRSA, nor does mention of the department or agency names imply endorsement by the US government.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (NP, XC, YT, HH, BH, JB, AS); acquisition of data (XC, CL, WZ, HH); analysis and interpretation of data (NP, XC, YT, WZ, HH, BH, JB, AS); drafting of the manuscript (NP, CL, HH); critical revision of the manuscript for important intellectual content (NP, XC, YT, CL, WZ, HH, BH, AS); statistical analysis (XC, WZ); provision of patients or study materials (WZ); obtaining funding (NP); administrative, technical, or logistic support (CL); and supervision (NP, HH, JB, AS).

 Nadereh Pourat, PhD, MSPH, UCLA Center for Health Policy Research, 10960 Wilshire Blvd, Ste 1550, Los Angeles, CA 90024. Email: pourat@ucla.edu.

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This paper utilizes latent class analysis to identify subgroups of complex conditions and of super-utilizers among health center patients to inform clinically tailored efforts.