National cancer system characteristics and prostate cancer outcomes: A global analysis.

Abstract

24 Background: The number of prostate cancer cases globally is projected to double from 2020 to 2040. However, significant global variation exists in prostate cancer incidence and mortality. A more specific understanding of health system levers specific to prostate cancer may inform health system planning. Therefore, we used global health system data from the World Health Organization (WHO), the World Bank, the Directory of Radiotherapy Centres (DIRAC), the United Nations Development Programme (UNDP), and the International Agency for Research on Cancer (IARC) to evaluate predictors of prostate cancer outcomes globally. Methods: National estimates of age-standardised incidence and mortality rates were obtained from the IARC 2022 database for male patients with prostate cancer of all ages (available for 185 countries). Mortality-to-incidence ratio (MIR), a proxy for survival, was calculated for each country. The following were taken from the World Bank: health spending as a percent of gross domestic product (GDP, 2021), physicians per 1000 population (2016-2021), nurses and midwives per 1000 population (2016-2021), surgical workforce per 1000 population (2013-2018), and GDP per capita. Universal healthcare (UHC index) and availability of pathology services were taken from the WHO. The gender inequality index (GII) and human development index (HDI) were taken from UNDP. Number of radiotherapy centers from DIRAC was used to derive number of radiotherapy centers per 1000 people. The association between prostate MIR and each of the ten metrics was evaluated using univariable linear models; Bonferroni corrected P<0.005 associations were included in the multivariable regression (MVA). We then assessed multicollinearity through variation inflation factor (VIF) analysis (VIF>10 excluded). α=0.10 defined statistical significance in the MVA. R2 evaluated goodness of fit. Results: Data availability ranged from 144 (77.8%, surgical workforce per 1000 population known) to 185 (100%, GDP per capita, RT centers per 1000 population). On univariable analysis, each of the ten metrics were significantly associated with MIR of prostate cancer (<0.001 for all). In MVA (N=123), VIF for HDI was 19.06. The final model (N=123 countries) with nine metrics had an R2 of 0.84. Therefore, the following variables were associated with lower (improved) MIR for prostate cancer: 1) surgical workforce per 1000 population, 2) UHC index, 3) radiotherapy centers per 1000 population, 4) GDP per capita. MIR plotted individually against these four metrics illustrates these associations. Conclusions: Analysis of global data and health-system metrics suggest that surgical workforce, availability of radiotherapy centers, degree of UHC, and GDP per capita are associated with improved prostate cancer outcomes. In leveraging individual countries’ health systems as data points, these findings may guide health system planning and prioritization.