Ignite Creation Date:
2024-05-06 @ 3:49 PM
Last Modification Date:
2024-10-26 @ 1:57 PM
Study NCT ID:
NCT04764435
Status:
COMPLETED
Last Update Posted:
2021-03-16
First Post:
2021-02-09
Brief Title:
The Effect of Physician Ownership on Dialysis Outcomes
Sponsor:
University of Southern California
Organization:
University of Southern California
Study Overview
Official Title:
The Effect of Physician Ownership on Dialysis Outcomes
Status:
COMPLETED
Status Verified Date:
2021-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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
Detailed Description:
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 eg KtV 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 ie 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 facilitys population eg 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
Patientstaff 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 ie Davita Fresenius
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 facilitys population eg 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
Patientstaff 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 ie Davita Fresenius
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| K08DK118213 | NIH | None | httpsreporternihgovquickSearchK08DK118213 |