Chronic Kidney Disease (CKD) induced Hyperparathyroidism (HPT), Hyperphosphatemia (HP), and Hyperkalemia (HK) – Epidemiology Forecast to 2030

Diagnosed prevalent cases of chronic kidney disease (CKD) in the seven major markets (7MM*) combined is expected to increase from 8.57 million cases in 2020 to 9.44 million in 2030, at an annual growth rate (AGR) of 1.02%, according to GlobalData, a leading data and analytics company.

Of the 7MM, the diagnosed prevalent cases of CKD in the US are expected to increase at an AGR of 1.59% between 2020 and 2030, followed by Spain (1.51%), and France (1.19%).

The major drivers for the spike in the diagnosed prevalent cases of CKD in the US and the 7MM combined is attributable to the aging population combined with population genetics and other risk factors. Factors such as race, gender, age and family history are highly linked to CKD. Moreover, smoking, obesity, hypertension and diabetes mellitus can also increase the risk for CKD.

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Suneedh Manthri, Associate Project Manager of Epidemiology at GlobalData, comments: “CKD is largely an asymptomatic condition that damages the kidneys and leads to the loss of kidney function over time. Currently, there is no cure for CKD; however, treatments can slow the progression of the disease. In the advanced stages of the disease CKD patients are advised to undergo renal replacement therapy (RRT) when the kidneys fail. Another way to reduce the burden of CKD would be early intervention. In order to achieve this, it is important to identify individuals with increased risk for CKD. Determining serum creatinine levels and urinalysis in patients with CKD risk will usually be sufficient for initial screening.”

*7MM: The US, France, Germany, Italy, Spain, the UK, and Japan

Scope

Chronic kidney disease (CKD), or chronic renal disease, is a condition characterized by a gradual loss of kidney function over time. In the early stages, CKD is a largely asymptomatic condition that damages the kidneys and leads to the loss of kidney function over time (Centers for Disease Control and Prevention, 2019). As the disease progresses, the symptoms worsen and eventually lead to kidney failure (Centers for Disease Control and Prevention, 2019). The glomerular filtration rate (GFR), a key measure of kidney function, is determined by the amount of creatinine in the blood, and the Kidney Disease Improving Global Outcomes (KDIGO) classification system is considered as the standard for GFR measurement and diagnosis of CKD (Levin et al., 2013).

This report provides an overview of the risk factors, comorbidities, and the global and historical epidemiological trends for CKD in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Japan). The report includes a 10-year epidemiology forecast for the diagnosed and total prevalent cases of CKD. The diagnosed and total prevalent cases of CKD are segmented by age (18 years and older), sex, and stage. The diagnosed prevalent cases of CKD are segmented based on dialysis-dependent and non-dialysis-dependent cases. The dialysis dependent cases are further segmented by hemodialysis-dependent and peritoneal dialysis-dependent. Additionally, the diagnosed prevalent cases of CKD were further segmented by hyperparathyroidism, hyperphosphatemia, and hyperkalemia among dialysis-dependent and non-dialysis-dependent cases. This epidemiology forecast for CKD is supported by data obtained from peer-reviewed articles and population-based studies:

  • The CKD epidemiology report and model were written and developed by Masters- and PhD-level epidemiologists.
  • The Epidemiology Report is in-depth, high quality, transparent and market-driven, providing expert analysis of disease trends in the 7MM.
  • The Epidemiology Model is easy to navigate, interactive with dashboards, and epidemiology-based with transparent and consistent methodologies.
  • Moreover, the model supports data presented in the report and showcases disease trends over a 10-year forecast period using reputable sources.

Reasons to buy

The CKD Epidemiology series will allow you to:

  • Develop business strategies by understanding the trends shaping and driving the global CKD market.
  • Quantify patient populations in the global CKD market to improve product design, pricing, and launch plans.
  • Organize sales and marketing efforts by identifying the age groups that present the best opportunities for CKD therapeutics in each of the markets covered.
  • Understand magnitude of CKD by stage, hemodialysis-dependent and peritoneal dialysis-dependent; hyperparathyroidism, hyperphosphatemia, and hyperkalemia among dialysis-dependent and non-dialysis-dependent cases of CKD.

Table of Contents

| About GlobalData

1 CKD – Hyperparathyroidism, Hyperphosphatemia, and Hyperkalemia: Executive Summary

1.1 Catalyst

1.2 Related Reports

1.3 Upcoming Reports

2 Epidemiology

2.1 Disease Background

2.2 Risk Factors and Comorbidities

2.3 Global and Historical Trends

2.4 7MM Forecast Methodology

2.4.1 Sources

2.4.2 Forecast Assumptions and Methods

2.4.3 Forecast Assumptions and Methods: Diagnosed Prevalent Cases of CKD – 7MM

2.4.4 Forecast Assumptions and Methods: Total Prevalent Cases of CKD

2.4.5 Forecast Assumptions and Methods: Diagnosed and Total Prevalent Cases of CKD by Stage

2.4.6 Diagnosed Prevalent Cases of CKD Based on Dialysis Dependence

2.4.7 Diagnosed Prevalent Cases of CKD with Hyperparathyroidism Among Dialysis-Dependent and Non-Dialysis-Dependent CKD Cases

2.4.8 Diagnosed Prevalent Cases of CKD with Hyperphosphatemia Among Dialysis Dependent and Non-dialysis Dependent CKD Cases

2.4.9 Diagnosed Prevalent Cases of CKD with Diagnosed Prevalent Cases of CKD with Hyperkalemia Among Dialysis-Dependent and Non-Dialysis-Dependent CKD Cases

2.5 Epidemiological Forecast for Chronic Kidney Disease Hyperparathyroidism, Hyperphosphatemia and Hyperkalemia (2020-2030)

2.5.1 Diagnosed Prevalent Cases of CKD

2.5.2 Age-Specific Diagnosed Prevalent Cases of CKD

2.5.3 Sex-Specific Diagnosed Prevalent Cases of CKD

2.5.4 Diagnosed Prevalent Cases of CKD by Stage

2.5.5 Diagnosed Prevalent Cases of CKD Based on Dialysis Dependence

2.5.6 Diagnosed Prevalent Cases of CKD with Hyperparathyroidism Among Dialysis-Dependent and Non-dialysis-Dependent CKD Cases

2.5.7 Diagnosed Prevalent Cases of CKD with Hyperphosphatemia Among Dialysis-Dependent and Non-Dialysis-Dependent CKD Cases

2.5.8 Diagnosed Prevalent Cases of CKD With Hyperkalemia Among Dialysis-Dependent and Non-Dialysis-Dependent CKD Cases

2.5.9 Total Prevalent Cases of CKD

2.5.10 Age-Specific Total Prevalent Cases of CKD

2.5.11 Sex-Specific Total Prevalent Cases of CKD

2.5.12 Total Prevalent Cases of CKD by Stage

2.6 Discussion

2.6.1 Epidemiological Forecast Insight

2.6.2 COVID-19 Impact

2.6.3 Limitations of the Analysis

2.6.4 Strengths of the Analysis

3 Appendix

3.1 Bibliography

3.2 About the Authors

3.2.1 Epidemiologist

3.2.2 Reviewers

3.2.3 Global Director of Therapy Analysis and Epidemiology

3.2.4 Global Head and EVP of Healthcare Operations and Strategy

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List of Tables

Table 1: Summary of Newly Added Data Types

Table 2: Summary of Updated Data Types

Table 3: KDIGO Classification of CKD

Table 4: Risk Factors and Comorbidities for CKD

List of Figures

Figure 1: 7MM, Diagnosed Prevalent Cases of CKD, Both Sexes, N, Ages ≥18 Years, 2020 and 2030

Figure 2: 7MM, Total Prevalent Cases of CKD, Both Sexes, N, Ages ≥18 Years, 2020 and 2030

Figure 3: 7MM, Diagnosed Prevalence of CKD, Men and Women, %, Ages ≥18 Years, 2020

Figure 4: 7MM, Total Prevalence of CKD, Men and Women, %, Ages ≥18 Years, 2020

Figure 5: 7MM, Sources Used to Forecast the Diagnosed Prevalent Cases of CKD

Figure 6: 7MM, Sources Used to Forecast the Diagnosed and Total Prevalent Cases of CKD by Stage

Figure 7: 7MM, Sources Used and Not Used to Forecast the Diagnosed Prevalent Cases of CKD Based on Dialysis Dependence

Figure 8: 7MM, Sources Used to Forecast the Diagnosed Prevalent Cases of CKD with Hyperparathyroidism, Hyperphosphatemia, and Hyperkalemia Among Dialysis-Dependent and Non-dialysis-Dependent CKD Cases

Figure 9: 7MM, Sources Used and Not Used to Forecast the Total Prevalent Cases of CKD

Figure 10: 7MM, Diagnosed Prevalent Cases of CKD, N, Both Sexes, Ages ≥18 Years, 2020

Figure 11: 7MM, Diagnosed Prevalent Cases of CKD by Age, N, Both Sexes, 2020

Figure 12: 7MM, Diagnosed Prevalent Cases of CKD by Sex, N, Ages ≥18 Years, 2020

Figure 13: 7MM, Diagnosed Prevalent Cases of CKD by Stage, N, Both Sexes, Ages ≥18 Years, 2020

Figure 14: 7MM, Diagnosed Prevalent Cases of CKD Based on Dialysis Dependence, N, Both Sexes, Ages ≥18 Years, 2020

Figure 15: 7MM, Diagnosed Prevalent Cases of CKD with Hyperparathyroidism, N, Both Sexes, Ages ≥18 Years, 2020

Figure 16: 7MM, Diagnosed Prevalent Cases of CKD With Hyperphosphatemia, N, Both Sexes, Ages ≥18 Years, 2020

Figure 17: 7MM, Diagnosed Prevalent Cases of CKD with Hyperkalemia, N, Both Sexes, ≥18 Years, 2020

Figure 18: 7MM, Total Prevalent Cases of CKD, N, Both Sexes, Ages ≥18 Years, 2020

Figure 19: 7MM, Diagnosed Total Cases of CKD by Age, N, Both Sexes, 2020

Figure 20: 7MM, Total Prevalent Cases of CKD by Sex, N, Ages ≥18 Years, 2020

Figure 21: 7MM, Total Prevalent Cases of CKD by Stage, N, Both Sexes, Ages ≥18 Years, 2020

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