Logo Medical Science Monitor

Call: +1.631.470.9640
Mon - Fri 10:00 am - 02:00 pm EST

Contact Us

Logo Medical Science Monitor Logo Medical Science Monitor Logo Medical Science Monitor

27 August 2020: Database Analysis  

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

Miguel Gallach ABCEF*#* , Miriam Mikhail Lette 1ABCDEFG* , May Abdel-Wahab 2EG , Francesco Giammarile 1EF , Olivier Pellet 1E , Diana Paez 1ABCDEFG

DOI: 10.12659/MSM.926544

Med Sci Monit 2020; 26:e926544

0 Comments

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.

Keywords: Cancer Care Facilities, Nuclear Medicine, Positron-Emission Tomography, Socioeconomic Factors, Global Health, Health Equity, Neoplasms, Positron Emission Tomography Computed Tomography, strategic planning

Background

There are alarming global inequities in access to nuclear medicine and diagnostic imaging. These are now showcased in an unprecedented, comprehensive manner in IMAGINE [1], the IAEA Medical imAGIng and Nuclear mEdicine global resources database. Launched in 2019, the database is being updated regularly and presents interactive maps on the country-by-country availability of medical imaging equipment and human resources (nuclear medicine physicians and radiologists). Having collated available databases, we now have a snapshot of global inequities in nuclear medicine. As an illustrative example, in Figure 1 we integrate the IMAGINE database for PET-CT scanners with the World Bank stratification of countries by income and illustrate the inequities among income groups. Thus, the population served by 1 PET-CT scanner in high-income countries (HICs) is approximately 601 000; 3 484 000 in upper-middle-income countries (UMICs); 10 000 000 in lower-middle-income countries (LMICs); and 166 667 000 in low-income countries (LICs).

While this database and the corresponding heat maps provide a valuable descriptive overview of the worldwide distribution of PET-CT scanners, they do not inform regarding the specific needs for PET-CT scanners per country. The question that many countries and public health organizations want to answer is how many PET-CT scanners are appropriate and needed to address the epidemiologic burden of a population.

In this article, we introduce a statistical model and its corresponding methodology, applied to the use of PET-CT scanners for the management of specific cancer types. It aims to assist countries in estimating their needs for PET-CT scanners. Firstly, we applied the proposed model to analyze the need for PET-CT scanners to evaluate patients with 6 cancer types (lung, colorectal, lymphoma, head and neck, melanoma, and esophagus) for which clinical guidelines recommend [18F]-FDG-PET-CT [2]. Secondly, a Bayesian model was used to rank the relative probability of an inhabitant lacking access to PET-CT services according to the country and world region where she or he resides. Our model serves as a springboard for strategic dialogue towards extending the population- and evidence-based benefits of medical imaging and nuclear medicine modalities in a stepwise, sustainable manner.

Material and Methods

ESTIMATING THE NUMBER OF PATIENTS WITHOUT ACCESS TO PET-CT SERVICES PER COUNTRY AND THE NUMBER OF PET-CT SCANNERS THAT A COUNTRY WOULD NEED: Our patient-centered model starts by defining pc, the number of cancer patients without access to PET-CT services in country c. The country-based cancer statistics were compiled from IARC (the International Agency for Research on Cancer) in GLOBOCAN (the Global Cancer Observatory) [3]. For the model, we selected 6 cancer types – lung, colorectal, lymphoma (Hodgkin’s and Non-Hodgkin’s), head and neck (lip, oral cavity, oro-naso-hypo-pharynx), melanoma, and esophageal – as these are clinical indications for which [18F]-FDG-PET-CT imaging is recommended by evidence-based guidelines [2], and covered by Medicare [4]. These patients are defined henceforth as “cancer patients”.

We define

where:

Table 1 summarizes the variables and parameters of our model, elaborated separately below.

RANKING COUNTRIES AND WORLD REGIONS:

An additional goal of this study was to develop a ranking system to identify countries that could benefit the most from upscaling PET-CT availability. To achieve this, we developed a naïve Bayesian approach that integrates epidemiological data into our estimation of PET-CT needs and can be used to rank countries or world regions according to the relative probability of a cancer patient lacking access to PET-CT services.

Our Bayesian model computes and defines the posterior probability of not having access to PET-CT services as:

Where P(C) is the probability of being from country C (computed as country population size/world population); P(I, n, p | C) is the conditional probability or the likelihood that a person is a 6-cancer patient, requires PET-CT service, and has no access to service given that is a citizen of country C, and P(I, n p) is the probability of having any of the 6 cancers, needing PET-CT service and not having access to it. I, n and p are the 6-cancer incidence, the number of patients who require PET-CT services, and the patients without access to PET-CT services in each country, respectively (Table 1). Population size is based upon World Bank data from 2018 [8].

All calculations, analyses, and figures were created in R (http://www.R-project.org/), Microsoft Excel (https://office.microsoft.com/excel), and Tableau software (http://www.tableau.com/).

Results

We integrated data from the IAEA IMAGINE database into the model and identified 96 countries requiring investment in PET-CT scanners in order to fully serve patients with the 6 common cancer types (Table 2). The model further estimates that the investment required to procure PET-CT scanners for the 96 countries is approximately USD$229.3M. We are only using the estimated cost of the 16-slice PET-CT scanner to compute the investment amount and not the associated cost necessary for its installation and operation, such as commissioning, maintenance, training, staffing, and other associated costs [9–16]. Nearly all modern PET-CT scanners are hybrid PET-CT units and, for the purpose of this analysis and based on PET-CT procurements by the IAEA in recent years, we estimated that the average cost of a 16-slice PET-CT scanner is USD$0.9M.

To illustrate the optimal utility of our Bayesian model, we first focused on lower-middle- and low-income countries (LMI and LI; Figure 2). According to our model, cancer patients from countries in these 2 income groups are more likely not to have access to PET-CT services (Table 2, Figure 2) compared to higher-resource settings. The model identifies 61 LMI and LI countries, out of the 72 from these income groups, that would require additional PET-CT scanners to ameliorate the PET-CT service deficits. These 61 countries include 34 countries from the WHO region Africa, 8 from Southeast Asia, 7 from the Western Pacific Region, 5 from the Eastern Mediterranean region, 4 from Europe, and 3 from the Americas. An investment of approximately $142M would meet the demand for procurement of 16 slice PET-CT scanners, to address the needs of patients with the 6 types of cancers. Dividing the number of countries per region by the investment needed per region, we can calculate the number of countries that would benefit from USD$1M invested (Figure 3).

Next, we focused on countries where cancer patients most likely lack access to PET-CT services. In Figure 4A, the 53 countries most likely to have inadequate PET-CT access for a cancer patient (posterior prob. >1e-03) are highlighted (excluding China). According to our model, all these countries would require investment in PET-CT scanners. We also show that an investment of USD$1M would have a greater impact in LI countries (Figure 4B) and in the Americas and African (sub-Saharan) regions (Figure 4C).

Finally, we focused on countries that have a similar number of PET-CT scanners per million inhabitants but face unequal service provision needs according to our model. In Figure 5 we show the frequency distribution of PET-CT scanner provision in units per million inhabitants for countries that would not require additional PET-CT scanners and those that would require additional units to meet their cancer patient needs according to our model. The overlapping region corresponds to those countries that have similar PET-CT scanner provision but for which our model identifies different requirements. For instance, the first overlapping bin includes 10 countries that have between 0.08 and 0.16 PET-CT scanners per million inhabitants. According to our model, 5 of the countries would not require additional PET-CT scanners, while the other 5, despite having similar provision, would require up to 16 additional PET-CT scanners to satisfy their specific cancer-patient demands.

Discussion

Reducing global inequities in access to nuclear medicine and diagnostic imaging remains a lofty goal. Stark inequities persist. To date, techniques to estimate broad population-based needs and benchmarks for upscaling PET-CT scanner numbers in low- and middle-income countries have proven elusive. We acknowledge that PET-CT scanners, the imaging modality used for this model, may not be the priority for some countries or regions. Countries first establish their use of medical imaging using modalities that are less costly, simpler to procure and use, and are more broadly applicable to an array of clinical indications, such as X-ray, ultrasound, and CT. However, the model that we present here can be adapted to analyze the specific needs for access to other medical imaging modalities, according to the socioeconomic, demographic, and epidemiological circumstances of each country. This would require more parameters and variables to be plugged into the model, as well as more assumptions. PET-CT was chosen first because most PET-CT exams are performed for cancer indications, and PET-CT thus is best-suited to development of the statistical template presented here.

A limitation of the study is that the investment estimation to satisfy the needs of cancer patients is incomplete, as we only considered the 16-slice PET-CT scanner costs. Without access to radionuclides, PET-CT scanning is impossible. A cyclotron must be within a few-hour radius of the PET-CT scanner, itself. Furthermore, radiotracers beyond [18F]-FDG would be needed for diseases not considered in this particular 6-cancer analysis. A discussion of the skilled human resources and availability of radionuclides for establishing or improving PET-CT provision are beyond the scope of this paper, as are the long-term maintenance, safety, and quality control requirements. To assist countries, the IAEA has published many relevant open-source documents, including ‘Cyclotron Produced Radionuclides: Guidelines for Setting Up a Facility’ [17] and ‘Planning a Clinical PET-CT Centre’ [18].

Other limitations of our model are the lack of mapping of existing PET-CT scanners within the individual countries’ private vs. public health systems, whether public health insurance covers PET-CT scans and where, and the relative distance of patients to the available PET-CT scanner(s), which are particularly important variables to consider for rural populations. The factors that marginalize patients from the provision of medical imaging services, even where the services exist, are variables to be included in future in-depth analyses, country-by-country, as the upscaling of PET-CT services is strategically planned.

Although these critical variables and their costs have yet to be included in the modelling, the analysis presented here does estimate the needs for sheer numbers of PET-CT scanners per country, for 6 common cancers, and is a step in the right direction towards bridging inequities in healthcare.

Conclusions

Here, we present a statistical model and its methodology, applied to the example of estimating the minimum requirements and costs to purchase PET-CT scanners to address the needs of patients with 6 selected types of cancer. Using clinical management guidelines and best practices, the same model could be applied to generate similar estimates for other medical imaging modalities and other triangulated indications based on specific demographics and epidemiological characteristics. The model described here could also be broadened to estimate the cost and socioeconomic impact of implementing any medical imaging modality for any clinical indication. The total cost of implementation, operation, and sustainability could also be included.

Implementation science promotes the use of interventions that have proven effective, supporting their integration into routine practice with the aim of improving population health. We hope that the statistical model presented in this article will serve as a stepping-stone, a synergistic companion to epidemiologic data and clinical management algorithms towards enabling countries to best meet the healthcare needs of their populations [19].

Figures

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

References

1. IAEA Medical imAGIng and Nuclear mEdicine (IMAGINE) https://humanhealth.iaea.org/HHW/DBStatistics/IMAGINE.html

2. Boellaard R, Delgado-Bolton R, Oyen WJG: Eur J Nucl Med Mol Imaging, 2015; 42; 328-54

3. International Association of Cancer Registries (GLOBOCAN) http://www.iacr.com.fr/index.php?option=com_content&view=article&id=101&Itemid=578

4. Hillner BE, Tosteson AN, Song Y: J Am Coll Radiol, 2012; 9; 33-41

5. Global cancer observatory https://gco.iarc.fr/

6. : Genesee/Finger Lakes Region PET Capacity and Utilization Report December, 2015 https://www.commongroundhealth.org/Media/Default/Publications/2015%20PET%20report%20FINAL-20160204040543.pdf

7. : A framework for the development of positron emission tomography (PET) services in England October, 2005, Department of health http://www.inahta.org/wp-content/uploads/2014/09/PET_A_framework_for_development_of_PET_services_in_England.pdf

8. World Bank Group https://data.worldbank.org/

9. Lissak RJ: Semin Nucl Med, 2000; 30; 299-305

10. Keppler JS, Conti PS: Am J Roentgenol, 2001; 77; 31-40

11. Conti PS, Keppler JS, Halls JM: Am J Roentgenol, 1994; 162; 1279-86

12. Frick MP, Gupta NC, Sunderland JJ: Semin Nucl Med, 1992; 22; 182-88

13. Buck AK, Herrmann K, Stargardt T: J Nucl Med Technol, 2010; 38; 6-17

14. Perini EA, Skopchenko M, Hong TT: Curr Radiopharm, 2019; 12; 187-200

15. Gerke O, Hermansson R, Hess S: PET Clin, 2015; 10; 105-24

16. Sloka JS, Hollett PD: Med Sci Monit, 2005; 11; PH1-6

17. Čomor J, Haji Saied M, Pillai MRA: Cyclotron produced radionuclides: guidelines for setting up a facility, 2009, International Atomic Energy Agency https://www-pub.iaea.org/MTCD/Publications/PDF/trs471_web.pdf

18. Abdul Khader MA, Amaral H, Belholavek O: Planning a clinical PET-CT centre, 2010, International Atomic Energy Agency https://www-pub.iaea.org/MTCD/Publications/PDF/Pub1457_web.pdf

19. World Health Organization (WHO) https://www.who.int/governance/eb/who_constitution_en.pdf

Figures

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

SARS-CoV-2/COVID-19

04 May 2022 : Clinical Research  

Effects of Wearing Face Masks on Exercise Capacity and Ventilatory Anaerobic Threshold in Healthy Subjects ...

Med Sci Monit In Press; DOI: 10.12659/MSM.936069  

22 April 2022 : Clinical Research  

Factors Associated with Falls During Hospitalization for Coronavirus Disease 2019 (COVID-19)

Med Sci Monit In Press; DOI: 10.12659/MSM.936547  

27 April 2022 : Meta-Analysis  

Effect of the COVID-19 Pandemic on Serum Vitamin D Levels in People Under Age 18 Years: A Systematic Review...

Med Sci Monit In Press; DOI: 10.12659/MSM.935823  

06 May 2022 : Clinical Research  

COVID-19: Another Cause of Dental Anxiety?

Med Sci Monit 2022; 28:e936535

In Press

19 May 2022 : Clinical Research  

Association Between Serum Homocysteine Levels and Severity of Diabetic Kidney Disease in 489 Patients with ...

Med Sci Monit In Press; DOI: 10.12659/MSM.936323  

18 May 2022 : Clinical Research  

Physical and Psychosocial Concept Domains Related to Health-Related Quality of Life (HRQL) in 50 Girls and ...

Med Sci Monit In Press; DOI: 10.12659/MSM.936801  

18 May 2022 : Clinical Research  

Association Between Variants of the Mannose-Binding Lectin 2 Gene and Susceptibility to Sepsis in the Haina...

Med Sci Monit In Press; DOI: 10.12659/MSM.936134  

17 May 2022 : Clinical Research  

Clinical Application of C-TIRADS Category and Contrast-Enhanced Ultrasound in Differential Diagnosis of Sol...

Med Sci Monit In Press; DOI: 10.12659/MSM.936368  

Most Viewed Current Articles

30 Dec 2021 : Clinical Research  

Retrospective Study of Outcomes and Hospitalization Rates of Patients in Italy with a Confirmed Diagnosis o...

DOI :10.12659/MSM.935379

Med Sci Monit 2021; 27:e935379

08 Mar 2022 : Review article  

A Review of the Potential Roles of Antioxidant and Anti-Inflammatory Pharmacological Approaches for the Man...

DOI :10.12659/MSM.936292

Med Sci Monit 2022; 28:e936292

01 Nov 2020 : Review article  

Long-Term Respiratory and Neurological Sequelae of COVID-19

DOI :10.12659/MSM.928996

Med Sci Monit 2020; 26:e928996

01 Jan 2022 : Editorial  

Editorial: Current Status of Oral Antiviral Drug Treatments for SARS-CoV-2 Infection in Non-Hospitalized Pa...

DOI :10.12659/MSM.935952

Med Sci Monit 2022; 28:e935952

Your Privacy

We use cookies to ensure the functionality of our website, to personalize content and advertising, to provide social media features, and to analyze our traffic. If you allow us to do so, we also inform our social media, advertising and analysis partners about your use of our website, You can decise for yourself which categories you you want to deny or allow. Please note that based on your settings not all functionalities of the site are available. View our privacy policy.

Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750