Addressing Global Inequities in Positron Emission Tomography-Computed Tomography (PET-CT) for Cancer Management: A Statistical Model to Guide Strategic Planning

Abstract

BACKGROUND According to the World Health Organization (WHO), non-communicable diseases are responsible for 71% of annual global mortality. National governments and international organizations are increasingly considering medical imaging and nuclear medicine access data in strategies to address epidemiologic priorities. Our objective here was to develop a statistical model to assist countries in estimating their needs for PET-CT systems for the management of specific cancer types. MATERIAL AND METHODS We introduce a patient-centered statistical model based on country-specific epidemiological data, PET-CT performance, and evidence-based clinical guidelines for PET-CT use for cancer. The output of the model was integrated into a Bayesian model to rank countries or world regions that would benefit the most from upscaling PET-CT scanners. RESULTS We applied our model to the IMAGINE database, recently developed by the International Atomic Energy Agency (IAEA). Our model indicates that at least 96 countries should upscale their PET-CT services and more than 200 additional PET-CT scanners would be required to fulfill their needs. The model also provides quantitative evidence indicating that low-income countries would benefit the most from increasing PET-CT provision. Finally, we discuss several cases in which the standard unit [number of scanners]/[million inhabitants] to guide strategic planning or address inequities is misleading. CONCLUSIONS Our model may help in the accurate delineation and further reduction of global inequities in access to PET-CT scanners. As a template, the model also has the potential to estimate the costs and socioeconomic impact of implementing any medical imaging modality for any clinical application.

Figure 1

PET-CT Scanners per million inhabitants. Data from IAEA IMAGINE [1].

Figure 2

LMI and LI countries that could benefit the most from upscaling PET-CT scanners to address the burden of 6 cancer types. Using the WHO region classification and the World Bank income stratification. (A) World map showing the posterior probability of PET-CT scanner deficits. Greens: values below the median. Pinks: values above the median. Median: 1e-04. (B) Investment needed to overcome the deficit of PET-CT scanners. (C) Posterior probability of PET-CT service deficits per income group. Median gross national income per capita is also given for each group (data from the World Bank).

Figure 3

Number of LMI and LI countries that could benefit from an investment of $1M to procure PET-CT scanners.

Figure 4

Countries where cancer patients have the highest probability of lacking access to PET-CT services (Posterior prob. >1e-03). (A) World map highlighting these countries (excluding China). (B) Number of countries that could benefit per USD$1M investment in PET-CT units per income group. (C) Number of countries that could benefit per USD$1M investment in PET-CT units per WHO region.

Figure 5

Frequency of PET-CT scanner provision. Dark blue: countries that would not require additional PET-CT scanners to fulfill their cancer patient needs according to our model. Mean and median: 1.44 and 0.96 PET-CT scanners per million inhabitants, respectively. Light blue: countries that would require more PET-CT scanners according to our model. Mean and median: 0.04 and 10⁻⁰⁵ PET-CT scanners per million inhabitants, respectively. Note the overlapping region between both distributions, which expands from 0.04 to 0.8 PET-CT scanners per million inhabitants.0