Measuring Comorbidity in Patients Receiving Dialysis: Can We Do Better?

Association of Hospitalization and Mortality Among Patients Initiating Dialysis With Hemodialysis Facility Ownership and Acquisitions

IMPORTANCE

Mergers and acquisitions among health care institutions are increasingly common, and dialysis markets have undergone several decades of mergers and acquisitions.

OBJECTIVE

To examine the outcomes of hemodialysis facility acquisitions independent of associated changes in market competition resulting from acquisitions.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study using difference-in-differences (DID) analyses to compare changes in health outcomes over time among in-center US dialysis facilities that were acquired by a hemodialysis chain with facilities located nearby but not acquired. Multivariable Cox proportional hazards regression models and negative binomial models with predicted marginal effects were developed to examine health outcomes, controlling for patient, facility, and geographic characteristics. All facility ownership types were examined together and stratified analyses were conducted of facilities that were independently owned and chain owned prior to acquisitions. The study was conducted from January 2001 to September 2015; 174 905 patients starting in-center dialysis in the 3 years before and following dialysis facility acquisitions were included. Data were analyzed from March 2017 to December 2018.

EXPOSURES

Acquisition by a hemodialysis chain.

MAIN OUTCOMES AND MEASURES

Twelve-month hazard of death and hospital days per patient-year were the primary outcomes.

RESULTS

Of the 174 905 patients included in the study, 79 705 were women (45.6%), 24 409 (14.0%) were of Hispanic ethnicity, 61 815 (35.3%) were black, 105 272 (60.2%) were white, and 1247 (0.7%) were Native American. Mean (SD) age was 65 (15) years. Before acquisitions, adjusted mortality and hospitalization rates were 10% (95% CI, -16% to -5%) and 2.9 days per patient-year (95% CI, -3.8 to -2.0) lower, respectively, at independently owned facilities that were acquired compared with those that were not acquired, while hospitalization rates were 0.7 days (95% CI, -1.2 to -2.0) lower at chain-owned facilities that were acquired compared with those that were not acquired. In stratified analyses of independently owned facilities, mortality decreases were smaller at acquired (-8.4%; 95% CI, -14% to -25%) vs nonacquired (-20.3%; 95% CI, -25.8% to -14.3%) facilities (DID P < .001). Similarly, hospitalization rates did not change at acquired facilities and decreased by 2.6 days per patient-year (95% CI, -3.6 to -1.7 days) at nonacquired facilities (DID P < .001). Acquisitions were not associated with changes in health outcomes at chain-owned facilities. Slower reductions in mortality and hospitalization rates at independently owned facilities contributed to significant differences in hospitalizations (-2.0 days; 95% CI, -2.5 to -1.6, at nonacquired vs 0.9 days; 95% CI, -1.3 to -0.5, at acquired facilities; DID, P < .001) across all ownership types but not mortality (DID, P = .28) with regard to acquisitions.

CONCLUSIONS AND RELEVANCE

Acquisition of independently owned dialysis facilities by larger dialysis organizations was associated with slower decreases in mortality and hospitalization rates, as nonacquired facilities appeared to experience more rapid improvements in outcomes over time.

The Role of Big Data in the Development and Evaluation of US Dialysis Care

Rapid growth in electronic communications and digitalization, combined with advances in data management, analysis, and storage, have led to an era of "Big Data." The Social Security Amendments of 1972 turned end-stage renal disease (ESRD) care into a single-payer system for most patients requiring dialysis in the United States. As a result, there are few areas of medicine that have been as influenced by Big Data as dialysis care, for which Medicare's large administrative data sets have had a central role in the evaluation and development of public policy for several decades. In the 1970/1980s, Medicare data helped identify concerning trends in costs, access to dialysis care, and quality of care delivered. As the research community and policymakers made Medicare's administrative data increasingly accessible for investigation, analyses of Medicare claims have had a large role in facilitating policy synthesis and refinement. Efforts to address the skyrocketing cost of injectable drugs in the 1990s and 2000s exemplify this expanded role of Big Data. Although there are opportunities for large government and nongovernmental administrative data sets to continue serving a critical role in the evaluation and development of ESRD policies, it is important to understand challenges and limitations associated with their use.

Health Insurance and the Use of Peritoneal Dialysis in the United States

BACKGROUND

Many patients in the United States have limited or no health insurance at the time they develop end-stage renal disease (ESRD). We examined whether health insurance limitations affected the likelihood of peritoneal dialysis (PD) use.

STUDY DESIGN

Retrospective cohort analysis of patients from the US Renal Data System initiating dialysis therapy in 2006 through 2012.

SETTING & PARTICIPANTS

We identified socioeconomically similar groups of patients to examine the association between health insurance and PD use. Patients aged 60 to 64 years with "limited insurance" (defined as having Medicaid or no insurance) at ESRD onset were compared with patients aged 66 to 70 years who were dually eligible for Medicare and Medicaid at ESRD onset.

PREDICTOR

Type of insurance coverage at ESRD onset.

OUTCOMES

The likelihoods of receiving PD before dialysis month 4, when all patients qualified for Medicare due to ESRD, and of switching to PD therapy following receipt of Medicare.

RESULTS

After adjusting for observable patient and geographic differences, patients with limited insurance had an absolute 2.4% (95% CI, 1.1%-3.7%) lower probability of PD use by dialysis month 4 compared with patients with Medicare at ESRD onset. The association between insurance and PD use reversed when patients became Medicare eligible; patients with limited insurance had a 3-fold higher rate of switching to PD therapy between months 4 and 12 of dialysis (HR, 2.9; 95% CI, 1.8-4.6) compared with patients with Medicare at ESRD onset.

LIMITATIONS

Because this study was observational, there is a potential for bias from unmeasured patient-level factors.

CONCLUSIONS

Despite Medicare's policy of covering patients in the month that they initiate PD therapy, insurance limitations remain a barrier to PD use for many patients. Educating providers about Medicare reimbursement policy and expanding access to pre-ESRD education and training may help overcome these barriers.