Patients with Idiopathic Membranous Nephropathy: A Real-World Clinical and Economic Analysis of U.S. Claims Data

Tara A Nazareth1, Furaha Kariburyo2, Aaron Kirkemo1

  • 1Mallinckrodt Pharmaceuticals, Bedminster, New Jersey.

Abstract

Insights

Membranous nephropathy (MN) patients face high disease severity and costs, primarily driven by outpatient and inpatient care. Early interventions are crucial to improve outcomes and manage resource allocation before end-stage renal disease (ESRD).

Area of Science:

  • Nephrology
  • Health Economics
  • Real-World Evidence

Background:

  • Membranous nephropathy (MN) is a leading cause of nephrotic syndrome in adults, with a significant portion progressing to end-stage renal disease (ESRD).
  • Limited real-world data exists on the economic burden of MN on health plans.
  • Understanding MN patient outcomes is vital for developing effective management strategies.

Purpose of the Study:

  • To characterize clinical and economic outcomes in MN patients over one year.
  • To compare the top 5% highest-cost patients with the remaining 95%.

Main Methods:

  • Retrospective analysis of 2,689 commercially insured patients with MN using administrative claims data (2012-2015).
  • Evaluation of clinical characteristics, healthcare resource utilization (HCRU), and costs for one year post-diagnosis.
  • Comparison of high-cost cohort (HCC, top 5%) versus non-high-cost cohort (NHCC, 95%).

Main Results:

  • HCC patients exhibited higher disease severity, adverse outcomes, and significantly greater HCRU (inpatient, ED, outpatient visits, prescriptions) than NHCC patients.
  • Total MN-related costs reached $123.2 million, with HCC patients accounting for 43.7% of costs ($401,608 mean per patient) versus NHCC patients (56.3%, $27,154 mean per patient).
  • Outpatient visits, inpatient stays, and prescriptions were the primary cost drivers for both cohorts.

Conclusions:

  • MN patients experience substantial disease burden, leading to significant HCRU and healthcare costs.
  • Outpatient settings, followed by inpatient and prescriptions, are key cost drivers for MN.
  • Further research into modifiable factors is needed to guide early interventions and optimize resource allocation, preventing progression to ESRD.

Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
3.2K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.9K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.9K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
5.6K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
690