Spinal Muscular Atrophy: Epidemiology Forecast To 2028

Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disease characterized by degeneration of alpha motor neurons in the spinal cord, resulting in progressive proximal muscle weakness and paralysis. The most common form of SMA is 5q SMA, linked to chromosome 5q and its phenotype is classified into four grades of severity (type I, type II, type III, and type IV) based on age of onset and motor function achieved (Amico et al., 2011). Additionally, SMA type 0 is an uncommon form of very severe SMA with prenatal onset (Finkel et al., 2015). In rare cases SMA is also caused by the mutation in another gene and known as non-5q SMA (Verhaart, Robertson, Wilson, et al., 2017).

GlobalData epidemiologists utilized historical data obtained from Orphanet, peer-reviewed articles, and population-based studies to build the forecast for the diagnosed prevalent cases of SMA in the 7MM. GlobalData epidemiologists applied the prevalence of SMA drawn from the above sources to each country’s population to calculate the number of estimated diagnosed prevalent cases for each subtype respectively.

The following data describes epidemiology of SMA. GlobalData epidemiologists forecast an increase in the diagnosed prevalent cases of SMA in the 7MM from 25,783 diagnosed prevalent cases in 2018 to 26,690 diagnosed prevalent cases in 2028, with an Annual Growth Rate (AGR) of 0.35% during the forecast period. The US will have the highest number of diagnosed prevalent cases of SMA among the 7MM, while Spain will have the lowest. In the 7MM combined, type II SMA made up the highest proportion of diagnosed prevalent cases of SMA and type IV made up the lowest proportion of diagnosed prevalent cases of SMA in 2018.

Scope

The Spinal Muscular Atrophy Epidemiology Report and Model provide an overview of the risk factors and global trends of Spinal Muscular Atrophy (SMA) in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK and Japan).

The report includes a 10-year epidemiological forecast for the diagnosed prevalent cases of SMA segmented by sex (for all ages) in these markets. The diagnosed prevalent cases of SMA are further segmented by type (type I, type II, type III and type IV). Additionally, the model includes a 10-year epidemiological forecast for the diagnosed prevalent cases of non-5q SMA.

The SMA 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 SMA Epidemiology series will allow you to:

Develop business strategies by understanding the trends shaping and driving the global SMA market.

Quantify patient populations in the global SMA market to improve product design, pricing, and launch plans.

Organize sales and marketing efforts by identifying the age groups and sex that present the best opportunities for SMA therapeutics in each of the markets covered.

Understand magnitude of SMA population by its type.

Table of Contents

1 Table of Contents

1.1 List of Tables

1.2 List of Figures

2 Spinal Muscular Atrophy: Executive Summary

2.1 Related Reports

2.2 Upcoming Reports

3 Epidemiology

3.1 Disease Background

3.2 Risk Factors and Comorbidities

3.3 Global and Historical Trends

3.4 Forecast Methodology

3.4.1 Sources

3.4.2 Forecast Assumptions and Methods

3.4.3 Forecast Assumptions and Methods – Diagnosed Prevalent Cases of SMA

3.5 Epidemiological Forecast for SMA (2018–2028)

3.5.1 Diagnosed Prevalent Cases of SMA

3.5.2 Sex-Specific Diagnosed Prevalent Cases of SMA

3.5.3 Diagnosed Prevalent Cases of SMA by Type

3.6 Discussion

3.6.1 Epidemiological Forecast Insight

3.6.2 Limitations of Analysis

3.6.3 Strengths of Analysis

4 Appendix

4.1 Bibliography

4.2 About the Authors

4.2.1 Epidemiologists

4.2.2 Reviewers

4.2.3 Global Director of Therapy Analysis and Epidemiology

4.2.4 Global Head and EVP of Healthcare Operations and Strategy

4.3 About GlobalData

4.4 Contact Us

4.5 Disclaimer

List of Tables

Table 1: Risk Factors and Comorbidities for SMA

List of Figures

Figure 1: 7MM, Diagnosed Prevalent Cases of SMA, N, Both Sexes, All Ages, 2018 and 2028

Figure 2: 7MM, Diagnosed Prevalence of SMA, %, Both Sexes, All Ages, 2018

Figure 3: 7MM, Sources Used and Not Used to Forecast the Diagnosed Prevalent Cases of SMA

Figure 4: 7MM, Diagnosed Prevalent Cases of SMA, N, Both Sexes, All Ages, 2018

Figure 5: 7MM, Sex-Specific Diagnosed Prevalent Cases of SMA, N, All Ages, 2018

Figure 6: 7MM, Diagnosed Prevalent Cases of SMA by Type, N, Both Sexes, All Ages, 2018

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