The Effect of Physician Ownership on Dialysis Outcomes

Many dialysis facilities have financial relationships with nephrologists, including joint venture agreements, where the nephrologist owns a minority share of the dialysis facility. Such agreements could present a conflict of interest with respect to patient care. This study will investigate whether these joint venture agreements are associated with differences in the quality of care provided by dialysis facilities.

The investigators will use the United States Renal Data System (USRDS), a registry of all patients on dialysis in the US irrespective of payer. The dataset includes patient characteristics (biologic and sociodemographic) within 45 days of initiating dialysis for all patients with ESKD irrespective of insurance coverage, death data for all patients with ESKD irrespective of insurance, dialysis facility characteristics, which are updated annually, longitudinal treatment data submitted by the end-stage renal disease (ESRD) networks for all patients irrespective of insurance, and CROWNWeb clinical data: monthly treatment data (e.g., Kt/V, treatment time, serum hemoglobin, vascular access) submitted by dialysis facilities for all patients irrespective of insurance. The registry is linked to Fee-for-service Medicare claims for all patients with this payer. All data have already been collected (i.e., this is a retrospective study) and deidentified by a data distributor.

The investigators have also obtained a cross-sectional dataset of dialysis facilities and physician owners in 2017 from the Centers for Medicare \& Medicaid Services (CMS) through a Freedom of Information Act request.

In this study, the investigators will link the USRDS to this cross-sectional dataset. The investigators will also link the data to publicly available data from Dialysis Facility Compare (which contains quality performance of each dialysis facility, published by the government on a quarterly basis) and Census data (which contains geographic sociodemographic characteristics).

From this data linkage, the investigators will study differences between facilities that are physician owned and those that are not physician owned. The investigators will study outcomes both at the facility level and the patient level.

All models will have an alpha of 5% and will have 2-sided statistical tests.

For facility level outcomes:

The investigators will construct a facility level dataset and compare physician-owned facilities to non-physician owned facilities adjusting for facility characteristics and regional (zipcode level) sociodemographic characteristics. The investigators will also test the effect of incorporating patient characteristics into the model. For patient characteristics, the investigators will take the average for each facility's population (e.g., average age, % of patients male, etc.). The investigators will use ordinary least squares for continuous outcomes and logistic regression for binary outcomes. The investigators will use robust standard errors.

For patient level outcomes:

The investigators will construct a patient-month panel dataset and compare patients dialyzing at physician-owned facilities to those dialyzing at non-physician owned facilities. The investigators will adjust for patient, facility, and zipcode level sociodemographic characteristics. Since all outcomes are binary, the investigators will use logistic regression for all models. The investigators' primary analysis will be logistic regression, adjusting for patient, facility, zipcode characteristics, with patient-level fixed effects and non-parametric bootstrap standard errors. In order, The investigators will explore the sensitivity of results to the following:

* logistic regression with all adjusters, patient-level fixed effects, and robust standard errors
* ordinary least squares with all adjusters, patient-level fixed effects, cluster-robust standard errors at the facility level
* ordinary least squares with all adjusters, patient-level fixed effects, robust standard errors

The investigators also pre-specify the adjusters below:

Patient characteristics (comorbidities will be obtained using the Chronic Conditions Warehouse software on a 12 month lookback of Medicare fee-for-service claims)

* Age
* Sex
* Race
* Ethnicity
* Prior transplant
* Incident patient (first 120 days of dialysis)
* Years with ESRD
* Dual Eligibility
* Hypertension
* Alzheimers
* Atrial fibrilation
* Prior myocardial infarction
* Asthma
* Breast Cancer
* Cataract
* Chronic obstructive pulmonary disease
* Colorectal Cancer
* Depression
* Diabetes
* Endometrial Cancer
* Glaucoma
* Congestive Heart Failure
* Hip Fracture
* Hyperlipidemia
* Hypertension
* Ischemic heart disease
* Lung cancer
* Osteoporosis
* Prostate Cancer
* Rheumatoid Arthritis / Osteoarthritis
* Prior stroke / transient ischemic attack
* Benign prostatic hyperplasia

Facility characteristics

* For-profit status
* Chain owned
* Number of patients at facility
* Patient:staff ratio
* ESRD Network

Regional (zipcode level) sociodemographic characteristics

* Median Income
* % of zipcode with high school degree
* % of zipcode below poverty line

We pre-specify a subgroup analysis by whether the dialysis facility is owned by a large dialysis organization (i.e., Davita, Fresenius).

Eligibility Criteria:
Inclusion Criteria:

* Adult
* Receiving dialysis for ESRD in the US
* Medicare fee for service as primary payer (and had Medicare FFS for at least 12 months prior to the month
* Received dialysis between 1/1/2017-12/31/2017
* Receiving dialysis in a free-standing facility
* No missing data

Exclusion Criteria:

* Pediatric (\< 18 years)
* Receiving dialysis in hospital-based facility
* Receiving dialysis in a facility that had \>= 15% pediatric patient months