DRG Epidemiology's coverage of acute myeloid leukemia comprises epidemiological estimates of key patient populations across 45 countries worldwide. We report both the incidence and prevalence of acute myeloid leukemia for each country, as well as annualized case counts projected to the national population.
Most patient populations are forecast over a period of 20 years for the major mature pharmaceutical markets and 10 years for the other countries covered in this report. In addition to forecasting incident and prevalent patient populations, the number of drug-treatment opportunities at specific lines of therapy are also forecast across the major mature pharmaceutical markets.
DRG Epidemiology's acute myeloid leukemia forecast will answer the following questions:
· In developing countries, what impact will economic growth and development have on the number of people diagnosed with acute myeloid leukemia per year?
· How will improvements in survival change the number of people living with a diagnosis of acute myeloid leukemia?
· Of all people diagnosed with acute myeloid leukemia, how many in each country across the major mature pharmaceutical markets are drug-treated?
· How will demographic trends, such as population aging and improving life expectancy, affect the epidemiology of acute myeloid leukemia over the forecast period?
All forecast data are available on the DRG Insights Platform in tabular format, with options to download to MS Excel. All populations are accompanied by a comprehensive description of the methods and data sources used, with hyperlinks to external sources. A summary evidence table generated as part of our systematic review of the epidemiological literature is also provided for full transparency into research and methods. In addition, we provide a graphical depiction of the patient flow between or within different disease states for the countries considered in this report. These patient flow diagrams are provided at the regional level, but may be requested for any specific country or forecast year.
In addition to the total number of cases for each forecast year, DRG Epidemiology also provides at least ten years of forecast data for the following acute myeloid leukemia subpopulations:
· Diagnosed incident cases by FLT3 mutation status
· 1st line drug-treatable population- AML (excluding APL)
· 2nd line drug-treatable population- AML (excluding APL)
· 3rd line drug-treatable population- AML (excluding APL)
· 1st line fit and unfit drug treatable population- AML (excluding APL)
· 1st line fit drug treated and non-drug treated population- AML (excluding APL)
· 1st line unfit drug treated and non-drug treated population- AML (excluding APL)
· 1st line drug treated and non-drug treated population- AML (excluding APL)
· 2nd line drug-treated and non-drug treated population- AML (excluding APL)
· 3rd line drug-treated and non-drug treated population- AML (excluding APL)
Note: coverage may vary by country and region.
- Mature Markets
- Key Findings
- On-Demand Content
- Diagnosed Incidence of Acute Myeloid Leukemia per 100,000 People of All Ages in 2017 and 2037
- Diagnosed Incidence of Acute Promyelocytic Leukemia per 100,000 People of All Ages per Year in 2017 and 2037
- Relative Sizes of the Factors Contributing to the Trend in Incident Cases of Acute Myeloid Leukemia over the Next 20 Years
- Relative Sizes of the Factors Contributing to the Trend in Incident Cases of Acute Promyelocytic Leukemia over the Next 20 Years
- Epidemiology Data
- Diagnosed Prevalent Cases
- Newly Diagnosed Incident Cases
- FLT-3 Mutation
- Drug-Treatable Populations
- Drug-Treated Populations
- Lifetime DALYs Gained
- Reference Materials
- Literature Review
- Risk/Protective Factors
Author(s): Mike Hughes, MSc, PhD; Atul Sharma, MPH
Mike joined Decision Resources as an epidemiologist in 2006. He has many years’ experience in the mathematical modeling of healthcare service delivery, cardiovascular and cancer epidemiology, biostatistics, meta-analysis and systematic reviewing. He has been principal author on many published articles in leading international journals in the areas of risk modeling in intensive care and cardiovascular medicine. He has also been responsible for developing national guidelines on behalf of NICE and the American College of Chest Physicians for the treatment of atrial fibrillation, stroke and hypertension. He is particularly interested in modeling patient flows in cancer and methods for forecasting disease burden in non-communicable epidemiology. Dr. Hughes received his in risk modeling in intensive care in 2003 from City University, London and is currently enrolled in a program in statistical causation and foundations of probability theory at the University of Nottingham.
Atul Sharma started working in Decision Resources Group as an intern in early 2016 and currently works as an associate epidemiologist. He performs fully documented systematic reviews of both published and grey literature on the epidemiology of assigned diseases and their risk factors to estimate incidence/prevalence over a 10-30 year period. He produces analyses for pharmaceutical drug developers on the descriptive epidemiology of major drug indications in mature and developing markets. He holds a Master’s in Public Health degree from School of Public Health, Post-Graduate Institute of Medical Education and Research and a Bachelor’s in dental surgery from MN DAV Dental College & Hospital.