Part Ⅰ Capitated Versus Fee-for-service Reimbursement And Quality Of Care For Chronic Disease: A US Cross-sectional Analysis

Abstract

1. Background

Upcoming alternative payment models Primary Care First (PCF) and Kidney Care Choices (KCC) incorporate capitated payments for chronic disease management. Prior research on the effect of capitated payments on chronic disease management has shown mixed results. We assessed the patient, physician, and practice characteristics of practices with capitation as the majority of revenue, and evaluated the association of capitated reimbursement with the quality of chronic disease care.

2. Methods

We performed a cross-sectional analysis of visits in the United States National Ambulatory Medical Care Survey (NAMCS) for patients with hypertension, diabetes, or chronic kidney disease (CKD). Our predictor was practice reimbursement type, classified as 1) majority capitation, 2) majority FFS, or 3) another reimbursement mix. Outcomes were quality indicators of hypertension control, diabetes control, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (ACEi/ARB) use, and statin use.

3. Results

About 9% of visits were to practices with the majority capitation revenue. Capitated practices, compared with FFS and other practices, had lower visit frequency (3.7 vs. 5.2 vs. 5.2, p=0.006), were more likely to be located in the West Census Region (55% vs. 18% vs. 17%, p<0.001), less likely to be solo practice (21% vs. 37% vs. 35%, p=0.005), more likely to be owned by an insurance company, health plan or HMO (24% vs. 13% vs. 13%, p=0.033), and more likely to have private insurance (43% vs. 25% vs. 19%, p=0.004) and managed care payments (69% vs. 23% vs. 26%, p<0.001) as the majority of revenue. The prevalence of controlled hypertension, controlled diabetes, ACEi/ARB use, and statin use was suboptimal across practice reimbursement types. Capitated reimbursement was not associated with differences in hypertension, diabetes, or CKD quality indicators, in multivariable models adjusting for patient, physician, and practice characteristics.

4. Conclusions

Practices with majority capitation revenue differed substantially from FFS and other practices with inpatient, physician, and practice characteristics, but were not associated with consistent quality differences. Our findings establish baseline estimates of chronic disease quality of care performance by practice reimbursement composition, informing chronic disease care delivery within upcoming payment models.

Keywords

Capitation, Fee-for-service, Physician reimbursement, Health services research, Chronic disease, Hypertension, Diabetes, Chronic kidney disease.

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Introduction

Chronic diseases, including hypertension, diabetes, and chronic kidney disease (CKD), cause a tremendous disease burden and increase the risk of cardiovascular events, kidney failure, and mortality [1, 2]. Quality of care in chronic disease management remains suboptimal, with less than half of individuals receiving evidence-based therapies and achieving disease control [3, 4]. For example, analyses of the National Health and Nutrition Examination Survey showed that only 43.7% of participants with hypertension had controlled blood pressure<140/90mmHg, and only 50.5% of participants with diabetes had controlled Hemoglobin A1c (HbA1c) values <7% [5, 6]. Emerging alternative payment models, which incorporate incentives for high-quality, cost-efficient care, aim to address quality of care gaps and rising expenditures in chronic disease management.

Although visit-based, fee-for-service (FFS) driven care remains the dominant reimbursement mechanism for outpatient visits in the US [7], capitation, which reimburses a set amount per patient per unit time, maybe a promising alternative payment mechanism to FFS care [8]. Capitated payments were first introduced in the 1980s to control costs [9], and are now making a resurgence as a method to increase flexibility in care delivery and emphasize outcomes rather than volume. Furthermore, in the context of the Coronavirus Disease 2019 (COVID-19) pandemic, capitated payments provide a consistent revenue stream, leaving physician practices less financially vulnerable to decreases in visit volumes [10]. Two voluntary payment models through the Center for Medicare & Medicaid Innovation (CMMI) feature capitated payments as a central component: Primary Care First (PCF) [11] and Kidney Care Choices (KCC) [12]. In addition to a fat FFS primary care visit fee, PCF provides a capitated per beneficiary per month payment, tiered according to the average level of comorbidities in the practice. KCC provides a capitated payment quarterly to nephrology practices for aligned beneficiaries with CKD Stages 4 and 5.

The anticipated impact of capitated payments on the quality of chronic disease care is unclear because prior studies examining the effect of capitated payments have shown mixed results [13]. Practices with predominantly capitated payments are less incentivized toward in-person visits, which may result in fewer opportunities to provide care and thus lower quality care [14, 15]. On the other hand, capitated payments coupled with quality incentives provide built-in compensation for non-visit-based care delivery, including care coordination, panel management, telephone calls, patient messaging, and other population health strategies, which may drive quality of care improvements.

The current reimbursement patterns of visits for patients with hypertension, diabetes, or CKD are currently not well understood. Therefore, using a national dataset of visits to office-based physicians, we examined the patient, physician, and practice characteristics of practices with capitation as the majority of revenue. We then assessed the variation in the quality of chronic disease care by practice reimbursement type. These results could inform how resources should be targeted to improve chronic disease care in the United States.

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Methods

1. Study design and population

We performed a serial cross-sectional analysis of visits to office-based ambulatory care physicians using data from the National Ambulatory Medical Care Survey (NAMCS). NAMCS is a federally funded survey conducted by the National Center for Health Statistics within the Centers for Disease Control and Prevention. NAMCS samples visit office-based physicians seeing ambulatory patients, including physicians with private offices rented within a hospital. NAMCS excludes physicians who are federally employed, those in military service, and those who treat patients only in institutional settings (nursing homes or hospitals). NAMCS utilizes a stratified two-stage probability sample which is fully described elsewhere [16]; first, physicians were sampled nationally, then a systematic random sample of visits was selected for each physician during an assigned week for inclusion in NAMCS. Field representatives from the U.S. Census Bureau interviewed physicians or practice representatives on physician and practice characteristics. Data on patient and visit characteristics were abstracted and recorded by field representatives from electronic or paper medical charts using a standardized data collection form, available on the Centers for Disease Control and Prevention website [16].

For our analysis, we included follow-up visits in NAMCS from 2012 to 2016 for adults (age≥18) with hypertension, Type 2 diabetes, or recognized CKD. Hypertension was defined as having an ICD-9 or ICD-10 code consistent with hypertension, receipt of antihypertensive medications, or a physician-reported diagnosis of hypertension in the medical chart. Diabetes was defined as having an ICD-9 or ICD-10 code for Type 2 diabetes, receipt of antidiabetic medications, or a physician-reported diagnosis of Type 2 diabetes. CKD was determined based on an ICD-9 or ICD-10 code for CKD, or a physician-reported diagnosis in the medical chart of “chronic renal failure” (2012–2013) or “chronic kidney disease” (2014–2016). Visits to the following specialties were included: general/family practice, internal medicine, cardiology, and medical “other specialties” according to the NAMCS classification. We did not include new patient visits (n=4501) because chronic disease management would be less attributable to the physician for new patients. We did not include visits to obstetrics and gynecology, general surgery, orthopedic surgery, dermatology, urology, psychiatry, neurology, ophthalmology, and otolaryngology, as these visits were unlikely to be primarily for chronic disease care. Pregnant adults (n=455) and patients with a diagnosis of end-stage kidney disease (n=10) were excluded.

2. Predictors and study covariates

Our predictor of interest was practice reimbursement composition, which was classified into three mutually exclusive categories: 1) majority (>50%) capitated payments, 2) majority (>50%) fee-for-service (FFS) revenue, or 3) another mix of fee-for-service, capitation, case rates (e.g. package pricing/episode of care), or other sources. Capitation reimbursement was assessed by asking “The following questions are about your practice revenue…Roughly, what percent of your patient care revenue comes from capitation?” FFS reimbursement was assessed by the question “Roughly, what percent of your patient care revenue comes from usual, customary, and reasonable fee-for-service?” Case rates were asked by the question “Roughly, what percent of your patient care revenue comes from case rates (e.g. package pricing/ episode of care)?” and other sources were assessed by asking “Roughly, what percent of your patient care revenue comes from other sources?”

Study covariates included patient characteristics and physician/practice characteristics. We assessed the following patient characteristics: age, sex, race (non-Hispanic white, non-Hispanic Black, Hispanic, non-Hispanic other), comorbidities (cancer, cerebrovascular disease, chronic obstructive pulmonary disease, congestive heart failure, coronary artery disease, depression, and obesity), a total of several chronic conditions, and patient payor type (private insurance, Medicare, Medicaid, or other). Comorbidities were defined by physician-reported diagnoses in the medical chart, except for obesity, which was defined as a body mass index of 30 kg/m2 or greater. Physician/practice characteristics included practice location (United States Census Region, metropolitan statistical area), practice size (solo or group), physician specialty (primary care or medical specialty care), physician compensation type (share of billings, fixed salary, mix, or other), practice ownership (physician, medical/academic health center, or insurance company/health plan/health maintenance organization), physician employment status (full owner, part owner, employee or contractor), practice payor mix (majority Medicare, majority Medicaid, majority private insurance, majority patient payments or other), and managed care contract revenue.

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3. Outcomes

The primary study outcomes were quality indicators of hypertension, diabetes, and CKD care. The hypertension quality indicator controlled hypertension with systolic blood pressure (BP) <140mmHg and diastolic BP <90mmHg, according to guidelines during the study period [17, 18]. Diabetes quality indicators were 1) controlled diabetes (HbA1c <7%) among persons with diabetes [19]; 2) angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (ACEi/ARB) use among those with hypertension and diabetes [20]; and 3) statin use in those with diabetes and aged 40–75 [21]. CKD quality indicators were 1) controlled hypertension (systolic BP <130mmHg and diastolic BP <80mmHg) among those with hypertension and CKD [17]; 2) controlled diabetes (Hemoglobin A1c [HbA1c] <7%) among persons with diabetes and CKD [19]; 3) ACEi/ARB use in those with hypertension and CKD [20]; and 4) statin use if age≥50 and CKD [22]. We performed sensitivity analyses varying the definition of controlled hypertension and defining controlled diabetes as an HbA1c <8% [23]. Medications were coded in NAMCS using Lexicon Plus, a comprehensive database of all prescription and some nonprescription drug products available in the U.S. [24]. The NAMCS dataset was fully de-identified and therefore was not subject to Institutional Review Board review.

4. Statistical analysis

We first compared patient and physician/practice characteristics for visits by patients with chronic disease by practice reimbursement type: 1) majority capitation, 2) majority FFS, and 3) another reimbursement mix. Differences in characteristics across practice reimbursement types were assessed using Wald tests to test the joint significance of the coefficients of a categorical variable being simultaneously equal to zero (Table 1). We additionally performed multivariable logistic regression to examine patient and physician/practice characteristics independently associated with having the majority of practice revenue from capitation.

We then calculated unadjusted chronic disease quality of care indicator performance, according to numerator and denominator definitions presented in Table 2, stratified by practice reimbursement type. We used multivariable logistic regression to evaluate the cross-sectional association between practice reimbursement type and chronic disease quality indicators using two nested models. Model 1 adjusted for patient characteristics, including age, sex, race, comorbidities, the total number of chronic conditions, and payor type to assess the association of reimbursement type with quality of care, after adjusting for differences in the patient population. Model 2 additionally adjusted for physician/practice characteristics, including practice location, practice size, physician specialty, physician compensation, practice ownership, and physician employment status, to control for structural factors other than reimbursement type that may influence chronic disease care quality. We accounted for multiple comparisons using a Bonferroni correction, where a two-sided p-value of 0.003 was considered statistically significant for our analyses.

All analyses incorporated patient visit weights and accounted for the ultimate cluster sampling in NAMCS, as described in NAMCS micro-data file documentation (Appendix 1). A total of 27% of visits for patients with hypertension, diabetes, or CKD had missing reimbursement information. To eliminate potential bias from complete case analysis, missing predictors and covariates were estimated using multiple imputations by chained equations using 50 imputations. Missingness of covariates was as follows: patient payor type (6%), physician compensation (5%), practice ownership (4%), physician employment status (3%), practice payor mix (14%), and managed care contract revenue (21%). Regression equations composed of all variables used in the fully adjusted models were created to impute missing values. We did not impute the outcomes of controlled hypertension or diabetes, as blood pressure and HbA1c laboratory values were thought to be missing not at random. We performed diagnostics using the Stata command midiagplots which showed good fitness of the generated data sets [26]. Data analyses were performed using Stata/IC, version 15.1 (StataCorp), and R version 4.0.2 statistical software, and all results presented are weighted estimates from the imputed dataset.

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Sri Lekha Tummalapalli 1,2,3, Michelle M. Estrella 3,4, Deanna P. Jannat‑Khah 5,6, Salomeh Keyhani 7 and Said Ibrahim 1

1. Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, Weill Cornell Medicine, 402 East 67th Street, New York, NY 10065, USA.

2. Division of Nephrology & Hypertension, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.

3. Kidney Health Research Collaborative, Department of Medicine, San Francisco Veterans Affairs Medical Center and the University of California, San Francisco, CA, USA.

4. Division of Nephrology, Department of Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.

5. Department of Medicine, Weill Cornell Medicine, New York, NY, USA.

6. Division of Rheumatology, Hospital for Special Surgery, New York, NY, USA.

7. Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.