EPMA Journal

, Volume 10, Issue 2, pp 137–152 | Cite as

The burden of prostate cancer is associated with human development index: evidence from 87 countries, 1990–2016

  • Rajesh SharmaEmail author



To examine the temporal patterns of the prostate cancer burden and its association with human development.

Subject and methods

The estimates of the incidence and mortality of prostate cancer for 87 countries were obtained from the Global Burden of Disease 2016 study for the period 1990 to 2016. The human development level of a country was measured using its human development index (HDI): a summary indicator of health, education, and income. The association between the burden of prostate cancer and the human development index (HDI) was measured using pairwise correlation and bivariate regression. Mortality-to-incidence ratio (MIR) was employed as a proxy for the survival rate of prostate cancer.


Globally, 1.4 million new cases of prostate cancer arose in 2016 claiming 380,916 lives which more than doubled from 579,457 incident cases and 191,687 deaths in 1990. In 2016, the age-standardised incidence rate (ASIR) was the highest in very high–HDI countries led by Australia with ASIR of 174.1/100,000 and showed a strong positive association with HDI (r = 0.66); the age-standardised mortality rate (ASMR), however, was higher in low-HDI countries led by Zimbabwe with ASMR of 78.2/100,000 in 2016. Global MIR decreased from 0.33 in 1990 to 0.26 in 2016. Mortality-to-incidence ratio (MIR) exhibited a negative gradient (r = − 0.91) with human development index with tenfold variation globally with seven countries recording MIR in excess of 1 with the USA recording the minimum MIR of 0.10.


The high mortality and lower survival rates in less-developed countries demand all-inclusive solutions ranging from cost-effective early screening and detection to cost-effective cancer treatment. In tackling the rising burden of prostate cancer predictive, preventive and personalised medicine (PPPM) can play a useful role through prevention strategies, predicting PCa more precisely and accurately using a multiomic approach and risk-stratifying patients to provide personalised medicine.


Prostate cancer Incidence Mortality Mortality-to-incidence ratio Human development index Predictive preventive personalised medicine Precision medicine 



We thank the Institute of Health Metrics and Evaluation (IHME) for making Global Burden of Disease (GBD) data pertaining to prostate cancer available in the public domain.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The research is conducted using data available in the public domain and does not include any human participants and/or animals.


  1. 1.
    Global Burden of Disease Cancer Collaborators. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2016 a systematic analysis for the Global Burden of Disease Study. JAMA Oncol. 2018;4:1553. Scholar
  2. 2.
    Dorr M, Holzel D, Schubert-Fritschle G, Engel J, Schlesinger-Raab A. Changes in prognostic and therapeutic parameters in prostate cancer from an epidemiological view over 20 years. Oncol Res Treat. 2015;38:8–14.CrossRefGoogle Scholar
  3. 3.
    Winter A, Sirri E, Jansen L, Wawroschek F, Kieschke J, Castro FA, et al. Comparison of prostate cancer survival in Germany and the USA: can differences be attributed to differences in stage distributions? BJU Int. 2017;119:550–9.CrossRefGoogle Scholar
  4. 4.
    Mohler JL, Bahnson RR, Boston B, Busby JE, D'Amico A, Eastham JA, et al. Prostate cancer: clinical practice guidelines in oncology. J Natl Compr Cancer Netw. 2010;8:162–200.CrossRefGoogle Scholar
  5. 5.
    Quinn M, Babb P. Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part II: individual countries. BJU Int. 2002 Jul 1;90(2):174–84.CrossRefGoogle Scholar
  6. 6.
    GBD 2016 Cause of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1151–210.CrossRefGoogle Scholar
  7. 7.
    Vostakolaei F, Karim-Kos HE, Janssen-Heijnen ML, Visser O, Verbeek AL, Kiemeney LA. The validity of the mortality to incidence ratio as a proxy for site-specific cancer survival. Eur J Pub Health. 2010;21:573–7.CrossRefGoogle Scholar
  8. 8.
    Sunkara V, Hebert JR. The colorectal cancer mortality-to-incidence ratio as an indicator of global cancer screening and care. Cancer 2015. 2015;121(10):1563–9.Google Scholar
  9. 9.
    Chen SL, Wang SC, Ho CJ, Kao YL, Hsieh TY, Chen WJ, et al. Prostate cancer mortality-to-incidence ratios are associated with cancer care disparities in 35 countries. Sci Rep. 2017 Jan 4;7:40003.CrossRefGoogle Scholar
  10. 10.
    Sharma R. Breast cancer incidence, mortality and mortality-to-incidence ratio (MIR) are associated with human development, 1990–2016: evidence from Global Burden of Disease Study 2016. Breast Cancer. 2019:1–18.
  11. 11.
    Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2017. Available from (Accessed: August 14–16, 2018).
  12. 12.
    GBD 2016 Disease and Injury, Incidence and Prevalence collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1211–59.CrossRefGoogle Scholar
  13. 13.
    Foreman KJ, Lozano R, Lopez AD, Murray CJ. Modeling causes of death: an integrated approach using CODEm. Popul Health Metrics. 2012;10(1).Google Scholar
  14. 14.
    Human Development Database: (Accessed: 30.6.2018 and 1.7.2018).
  15. 15.
    Crawford ED. Epidemiology of prostate cancer. Urology. 2003;62(6):3–12.CrossRefGoogle Scholar
  16. 16.
    Crawford ED, Robert Grubb AB III, Andriole GL Jr, Chen MH, Izmirlian G, Berg CD, et al. Comorbidity and mortality results from a randomized prostate cancer screening trial. J Clin Oncol. 2011;29:355–61.CrossRefGoogle Scholar
  17. 17.
    Roobol MJ, Kranse R, Bangma CH, van Leenders AG, Blijenberg BG, van Schaik RH, et al. Screening for prostate cancer: results of the Rotterdam section of the European randomized study of screening for prostate cancer. Eur Urol. 2013;64:530–9.CrossRefGoogle Scholar
  18. 18.
    Schröder FH, Hugosson J, Roobol MJ, Tammela TL, Zappa M, Nelen V, et al. Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. Lancet. 2014;384:2027–35.CrossRefGoogle Scholar
  19. 19.
    Arnsrud RG, Holmberg E, Lilja SJ, Hugosson J. Opportunistic testing versus organized prostate-specific antigen screening: outcome after 18 years in the Göteborg randomized population-based prostate cancer screening trial. Eur Urol. 2015;68:354–60.CrossRefGoogle Scholar
  20. 20.
    Smith RA, Cokkinides V, Brawley OW. Cancer screening in the United States, 2008: a review of current American Cancer Society guidelines and cancer screening issues. CA Cancer J Clin. 2008;58:161–79.CrossRefGoogle Scholar
  21. 21.
    Barry MJ, Mulley AJ Jr. Why are a high overdiagnosis probability and a long lead time for prostate cancer screening so important? J Natl Cancer Inst. 2009;101:362–3.CrossRefGoogle Scholar
  22. 22.
    Grossman DC, Curry SJ, Owens DK, Bibbins-Domingo K, Caughey AB, Davidson KW, et al. Screening for prostate cancer: US Preventive Services Task Force recommendation statement. Jama. 2018;19:1901–13.Google Scholar
  23. 23.
    Moyer VA. Screening for prostate cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med. 2012;157:120–34.CrossRefGoogle Scholar
  24. 24.
    Grech G, Zhan X, Yoo BC, Bubnov R, Hagan S, Danesi R, et al. EPMA position paper in cancer: current overview and future perspectives. EPMA J. 2015;6:9. Scholar
  25. 25.
    Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7:23.CrossRefGoogle Scholar
  26. 26.
    Janssens JP, Schuster K, Voss A. Preventive, predictive, and personalized medicine for effective and affordable cancer care. EPMA J. 2018;9:113–23. Scholar
  27. 27.
    Cheng T, Zhan X. Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA J. 2017;8:51–60. Scholar
  28. 28.
    Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9:77–102. Scholar
  29. 29.
    Horgan RP, Kenny LC. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynaecol. 2011;13:189–95.Google Scholar
  30. 30.
    Tomlins SA, Day JR, Lonigro RJ, Hovelson DH, Siddiqui J, Kunju LP, et al. Urine TMPRSS2: ERG plus PCA3 for individualized prostate cancer risk assessment. Eur Urol. 2016;70:45–53.CrossRefGoogle Scholar
  31. 31.
    Leyten GH, Hessels D, Jannink SA, Smit FP, de Jong H, Cornel EB, et al. Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol. 2014;65:534–42.CrossRefGoogle Scholar
  32. 32.
    Salagierski M, Schalken JA. Molecular diagnosis of prostate cancer: PCA3 and TMPRSS2: ERG gene fusion. J Urol. 2012;187:795–801.CrossRefGoogle Scholar
  33. 33.
    Catalona WJ, Partin AW, Sanda MG, Wei JT, Klee GG, Bangma CH, et al. A multi-center study of [− 2] pro-prostate-specific antigen (PSA) in combination with PSA and free PSA for prostate cancer detection in the 2.0 to 10.0 ng/mL PSA range. J Urol. 2011;185:1650–5.CrossRefGoogle Scholar
  34. 34.
    Huang YQ, Sun T, Zhong WD, Wu CL. Clinical performance of serum [-2] proPSA derivatives, % p2PSA and PHI, in the detection and management of prostate cancer. Am J Clin Exp Urol. 2014;2:343.Google Scholar
  35. 35.
    Loeb S, Sanda MG, Broyles DL, Shin SS, Bangma CH, Wei JT, et al. The prostate health index selectively identifies clinically significant prostate cancer. J Urol. 2015;193:1163–9.CrossRefGoogle Scholar
  36. 36.
    Tosoian JJ, Druskin SC, Andreas D, Mullane P, Chappidi M, Joo S, et al. Use of the Prostate Health Index for detection of prostate cancer: results from a large academic practice. Prostate Cancer Prostatic Dis. 2017;20:228–33.CrossRefGoogle Scholar
  37. 37.
    Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of cancer–analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343:78–85.CrossRefGoogle Scholar
  38. 38.
    Mucci LA, Hjelmborg JB, Harris JR, Czene K, Havelick DJ, Scheike T, et al. Familial risk and heritability of cancer among twins in Nordic countries. Jama. 2016;315:68–76.CrossRefGoogle Scholar
  39. 39.
    McGinley KF, Tay KJ, Moul JW. Prostate cancer in men of African origin. 2016 Nat Rev Urol. 2016;13:99–107.CrossRefGoogle Scholar
  40. 40.
    Parkin DM, Bray F. Evaluation of data quality in the cancer registry: principles and methods part II. Completeness. Eur J Cancer. 2009;45:756–64.CrossRefGoogle Scholar

Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2019

Authors and Affiliations

  1. 1.University School of Management and EntrepreneurshipDelhi Technological UniversityDelhiIndia

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