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. 2024 Dec 18;24:1526. doi: 10.1186/s12885-024-13247-w

Changes in essential cancer medicines and association with cancer outcomes: an observational study of 158 countries

Moizza Zia Ul Haq 1, Camila Heredia 1, Adelaide Buadu 1, Amal Rizvi 1, Aine Workentin 1, Nav Persaud 1,2,✉
PMCID: PMC11656608  PMID: 39696026

Abstract

Background

Cancer is a major cause of mortality worldwide, and differences in cancer mortality rates between countries are, in part, due to differences in access to cancer care, including medicines. National essential medicines lists (NEMLs) play a role in prioritization of healthcare expenditure and access to medicines. We examined the association between amenable cancer mortality and listing medicines used in the management of eight cancers (non-melanoma skin, uterine, breast, Hodgkin lymphoma, colon, leukemia, cervical, and testicular) in national essential medicines lists of 158 countries and summarized changes to the inclusion of cancer treatments in NEMLs.

Methods

We conducted a cross-sectional examination of NEMLs for 158 countries, which were obtained in May 2023. We identified medicines used to treat each of the eight cancers and determined the number of medicines listed by NEMLs for each cancer. We conducted multiple linear regressions to examine the association between the number of medicines listed on the NEMLs and cancer mortality.

Results

We found associations between cancer medicine listing and outcomes for six of the eight examined cancers (non-melanoma skin cancer (p = 0.001), uterine cancer (p = 0.006), breast cancer (p = 0.001), Hodgkin lymphoma (p = 0.021), colon cancer (p = 0.006), and leukemia (p = 0.002)), when adjusting for healthcare expenditure and population size.

Conclusion

There was an association between listing cancer medicines on NEMLs and cancer mortality. Further research is required to explore how cancer mortality may be impacted by other cancer interventions, as well as policies to improve equitable access to cancer care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-024-13247-w.

Keywords: Essential medicines, Cancer medicines, Access to medicines

Background

Cancer is a major cause of mortality globally – in 2020 alone, there were an estimated 19.3 million new cases of cancer and almost 10 million cancer deaths, and cancer incidence and mortality is rapidly increasing [1]. Differences in mortality rates between countries rates are due to differences in risk factors and access to cancer care, including medicines [1].

The World Health Organization (WHO) has developed and updated Model Lists of Essential medicines, consisting of medicines “intended to be available in functioning health systems at all times, in appropriate dosage forms, of assured quality and at prices individuals and health systems can afford”, [2] which includes various cancer medicines. Several countries have developed their own national essential medicines lists (NEMLs), as per recommendations by the WHO to select essential medicines based on "disease burden, public health relevance, clinical effectiveness and safety, and cost-effectiveness” [3, 4]. These lists play a role in public procurement and supply and prioritization of healthcare expenditure [5]. Medicines deemed essential have been found to be more available than non-essential medicines in both the private and public sectors [6]. Furthermore, NEMLs influence essential medicine reimbursement schemes and in some countries may support the provision of essential medicines at no charge [5, 7]. Thus, NEMLs play a role in access to medicines, including cancer medicines, and may consequently impact cancer mortality.

The value of newer cancer treatments has been questioned since they are not always proven to reduce mortality [8–12]. Focusing on established treatments and basic care may be more important globally than expanding access to newer expensive treatments with questionable value [12]. We found only a weak association between the listing of cancer treatments in essential medicines lists in 2017 and relevant cancer outcomes [13].

The purpose of this study was to summarize changes to the inclusion of cancer treatments in NEMLs and to examine the association between amenable cancer mortality, measured by mortality-to-incidence ratios, and listing medicines used in the management of eight cancers in national essential medicines lists (NEMLs) of 158 countries.

Methods

Dataset sources

In May 2023, we obtained NEMLs by performing web searches and contacting experts, and extracted all the medicines listed in the NEMLs for each country. We identified 158 NEMLs, which were used to create an updated Global Essential Medicines (GEM) database of 2084 medicines (Persaud et al.: National essential medicine lists changes between 2017 and 2023: a descriptive study, submitted).

We obtained mortality-to-incidence ratios (MIRs) for each country for eight cancers (non-melanoma [NM] skin, uterine, breast, Hodgkin lymphoma, colon, leukemia, cervical, and testicular) from the 2019 Global Burden of Disease Study, which represent mortality amenable to health-care access and quality [14]. The mortality-to-incidence ratio (MIR) is calculated by dividing the mortality rate for a cancer by the incidence rate. Cancer mortality-to-incidence ratios utilize cancer registry data for each country [14, 15]. We chose these eight cancers because they had readily available mortality data [14].

Data collection

In December 2023, we searched the WHO Publications website (using the topics cancer, breast cancer, and cervical cancer) for international treatment guidelines for cancer management. We identified 2 guidelines for the management of breast cancer: the Global breast cancer initiative implementation framework, [16] and the WHO EMRO Technical Publication series 31-Guidelines for management of breast cancer, [17] and 4 guidelines for the management of cervical cancer: the Strategic framework for the comprehensive prevention and control of cervical cancer in the Western Pacific Region, [18] the Regional implementation framework for elimination of cervical cancer as a public health problem, [19] the Guide to essential practise for comprehensive cervical cancer control, [20] and 'Best buys' and other recommended interventions for the prevention and control of noncommunicable diseases [21]. We recorded the medicines used in the management of breast and cervical cancers from these 6 guidelines.

To obtain medicines for the management the other six cancers which did not have an international treatment guideline and to obtain additional medicines for the management of breast and cervical cancers, we retrieved medicines from the MEDI-HPS (MEDication Indication resource high precision subset) and the updated MEDI-2 HPS database [22, 23]. We searched using ICD-9-CM and ICD-10-CM diagnosis codes defining each of the eight cancers. We also conducted searches of the MIA database, which maps ICD (International Classification of Diseases) codes to ATC (Anatomical Therapeutic Chemical) medicine codes [24]. Finally, we searched the 2023 WHO Model List of Essential Medicines for medicines that were indicated for any of the eight cancers [2]. If a medicine appeared in any of the searches for a cancer guideline, MEDI-HPS, MIA, or 2023 WHO Model List of Essential Medicines, they were included in the initial list of medicines for that cancer. We created a final list of medicines (see Appendix) for each cancer that only included medicines if they were determined to be clinically appropriate in the management of the cancer. If more than 2 medicines from a drug class (based on ATC codes) appeared in the searches, all other drugs from that class were included in the final list if determined to be clinically appropriate as well. Medicines that were excluded were those used as supportive care, such as analgesics and bisphosphonates.

We coded the list of the medicines associated with each of the eight cancers into the GEM database and identified overlaps on these lists and on each country's NEML. We totalled the overlapping medicines to create a coverage score for each country per cancer cause. For instance, if a country listed 8 of the 14 medicines for NM skin cancer, that country received an NM skin cancer coverage score of 8. The coverage score accounted for the number of medicines by name, and did not account for different dosages or formulations. We obtained data for health expenditure from the WHO Global Health Observatory for the year 2021 [25]. Most of the data pertained to the year 2021; records from 2021 records were unavailable for two countries, so information from the nearest available year was accessed [26, 27]. We obtained population size data from the United Nations for the year 2021 [28]. The data are summarized in Table 1.

Table 1.

Country characteristics and cancer medicine coverage scores

Country NEML Year Health spending US$ per capita (2021) Population size UN (2021) Medicine coverage score: NM skin Medicine coverage score: uterine Medicine coverage score: breast Medicine coverage score: Hodgkin Medicine coverage score: colon Medicine coverage score: leukemia Medicine coverage score: cervical Medicine coverage score: testicular
Afghanistan 2015 81 40,099,462 2 1 3 0 1 2 0 1
Albania 2022 465 2,854,710 1 0 9 2 1 11 1 1
Algeria 2023 205 44,177,969 1 3 12 1 0 7 0 1
Angola 2021 64 34,503,774 9 2 14 10 3 20 3 9
Antigua and Barbuda 2022 923 93,220 6 2 17 7 3 14 2 4
Argentina 2021 1045 45,276,780 0 1 1 0 0 0 0 0
Armenia 2021 613 2,790,974 8 2 18 11 4 19 3 8
Australia 2023 7055 25,921,089 12 4 41 15 7 49 5 9
Bahrain 2015 1146 1,463,266 8 2 29 12 5 33 3 10
Bangladesh 2019 58 169,356,251 5 1 11 10 2 14 1 6
Belarus 2021 468 9,578,168 9 3 36 16 7 42 3 10
Benin 2018 35 12,996,895 6 2 21 9 3 18 2 5
Bhutan 2021 120 777,487 3 1 5 2 2 4 1 1
Bolivia (Plurinational State of) 2022 273 12,079,472 10 2 26 11 6 27 4 9
Bosnia and Herzegovina 2019 692 3,270,943 0 0 0 0 0 0 0 0
Botswana 2012 457 2,588,423 4 1 8 7 1 14 0 6
Brazil 2022 761 214,326,223 2 1 8 1 1 10 1 1
Bulgaria 2023 1040 6,885,868 1 3 18 1 2 18 1 1
Burkina Faso 2020 57 22,100,684 9 2 22 11 5 23 4 9
Burundi 2022 24 12,551,213 9 2 18 9 4 13 4 7
Cabo Verde 2018 248 587,925 9 2 18 10 3 17 4 9
Cambodia 2018 122 16,589,024 7 1 12 9 2 14 3 8
Cameroon 2022 64 27,198,628 9 2 29 14 5 29 4 10
Central African Republic 2017 43 5,457,155 5 1 13 10 3 11 1 5
Chad 2022 36 17,179,740 9 2 22 11 5 25 4 9
Chile 2006 1518 19,493,185 7 2 19 14 2 26 3 10
China 2018 671 1,425,893,465 8 2 15 8 4 23 3 7
Colombia 2019 558 51,516,562 9 3 33 14 6 35 3 10
Comoros 2014 99 821,626 5 1 11 13 2 16 1 7
Congo 2019 81 5,835,806 7 1 17 13 4 16 2 6
Cooks Islands 2017 737 17,003 0 1 3 0 0 1 1 1
Costa Rica 2019 949 5,153,957 8 2 24 10 5 26 4 10
Cote D'Ivoire 2020 82 27,478,249 9 3 31 15 6 27 3 10
Croatia 2022 1384 4,060,136 10 5 42 20 8 56 6 10
Cuba 2018 1186 11,256,373 9 3 28 12 6 34 3 10
Czechia 2012 2499 10,510,751 10 5 39 15 8 40 5 8
Democratic People's Republic of Korea 2012 0.5 25,971,909 1 0 5 2 1 4 1 2
Democratic Republic of the Congo 2020 22 95,894,119 5 1 7 5 1 6 1 3
Djibouti 2007 88 1,105,558 0 0 0 0 0 0 0 0
Dominica 2022 482 72,413 6 2 17 7 3 14 2 4
Dominican Republic 2018 417 11,117,874 9 2 20 13 3 18 4 10
Ecuador 2019 494 17,797,737 9 3 33 14 7 36 3 10
Egypt 2018 180 109,262,178 10 2 27 13 7 30 3 10
El Salvador 2020 442 6,314,168 8 2 17 11 4 20 4 9
Equatorial Guinea 2012 256 1,634,466 0 1 1 0 0 0 0 0
Eritrea 2010 25 3,620,312 5 1 7 7 1 12 0 4
Estonia 2012 2095 1,328,701 3 2 12 4 1 20 0 3
Eswatini 2012 280 1,192,271 5 1 7 5 1 5 0 3
Ethiopia 2020 26 120,283,026 9 2 25 14 5 32 3 10
Fiji 2015 250 924,610 8 2 13 9 2 14 2 6
Gabon 2019 234 2,341,179 7 1 16 9 7 16 4 7
Gambia 2019 25 2,639,916 4 1 8 5 1 8 2 2
Georgia 2007 417 3,757,980 5 1 10 11 2 15 1 7
Ghana 2017 100 32,833,031 5 1 10 6 0 12 1 6
Greece 2007 1846 10,445,365 11 6 35 14 7 40 4 8
Grenada 2022 505 124,610 6 2 17 7 3 14 2 4
Guatemala 2021 341 17,608,484 9 3 28 11 7 28 4 8
Guinea 2021 45 13,531,906 7 2 18 9 3 19 3 6
Guinea-Bissau 2020 69 2,060,721 9 2 19 10 3 26 3 8
Guyana 2021 471 804,567 10 2 18 8 4 17 3 6
Haiti 2020 58 11,447,569 10 2 25 14 6 27 4 10
Honduras 2018 254 10,278,346 8 2 23 12 7 21 4 10
Iceland 2022 6716 370,335 2 1 4 3 2 6 1 4
India 2022 74 1,407,563,842 10 2 23 12 5 26 3 10
Indonesia 2021 161 273,753,191 9 2 15 11 2 19 3 9
Iran (Islamic Republic of) 2017 393 87,923,433 10 4 38 21 7 52 4 10
Iraq 2014 249 43,533,593 10 3 28 15 6 36 4 10
Ireland 2023 6764 4,986,526 5 4 28 5 3 31 1 3
Jamaica 2015 372 2,827,695 11 2 23 14 4 24 3 10
Japan 2018 4347 124,612,531 0 0 1 2 0 4 0 0
Jordan 2021 299 11,148,278 10 2 25 14 5 32 3 10
Kazakhstan 2020 403 19,196,466 0 0 0 0 1 0 0 1
Kenya 2019 95 53,005,614 10 3 28 15 6 30 6 10
Kiribati 2009 262 128,874 3 1 5 2 1 3 0 2
Kyrgyzstan 2009 73 6,527,744 5 1 15 7 3 13 1 5
Latvia 2023 1898 1,873,919 2 1 22 7 4 28 1 3
Lebanon 2018 307 5,592,631 7 3 24 11 6 28 4 8
Lesotho 2005 115 2,281,455 4 0 4 3 2 5 0 3
Liberia 2022 112 5,193,416 2 1 6 4 1 4 1 3
Libya 2019 381 6,735,277 9 2 29 15 7 31 4 10
Lithuania 2012 1859 2,786,651 8 3 27 12 3 24 4 9
Madagascar 2019 18 28,915,653 8 2 22 13 5 22 4 10
Malawi 2015 47 19,889,742 8 2 15 12 2 15 3 9
Malaysia 2023 487 33,573,874 9 2 27 15 5 35 4 10
Maldives 2021 1039 521,458 10 5 37 15 7 32 6 9
Mali 2019 40 21,904,983 7 1 15 13 4 18 3 8
Malta 2022 3642 526,748 10 2 34 15 7 41 4 10
Marshall Islands 2007 767 42,050 0 1 2 0 0 0 0 0
Mauritania 2021 89 4,614,974 7 2 14 7 3 16 3 5
Mauritius 2022 565 1,298,915 7 1 22 12 4 22 3 10
Mexico 2017 611 126,705,138 10 5 44 17 8 44 4 10
Mongolia 2020 316 3,347,783 7 3 20 6 7 19 4 6
Montenegro 2020 985 627,859 10 2 36 14 7 40 5 9
Morocco 2017 221 37,076,585 9 2 22 11 8 25 3 9
Mozambique 2017 45 32,077,072 9 2 15 9 3 21 4 9
Myanmar 2016 65 53,798,085 10 2 29 14 6 34 4 10
Namibia 2016 456 2,530,151 8 1 16 12 3 22 2 9
Nauru 2010 1530 12,512 0 1 3 0 0 1 0 1
Nepal 2021 65 30,034,990 10 2 21 13 3 24 4 10
Nicaragua 2011 198 6,850,540 7 1 10 8 2 16 2 9
Niger 2018 34 25,252,722 3 1 9 5 3 7 2 3
Nigeria 2020 84 213,401,323 10 2 28 13 5 30 4 10
Niue 2006 1912 1,937 0 1 2 0 0 0 0 0
North Macedonia 2015 560 2,103,330 9 1 12 11 2 18 3 10
Oman 2020 853 4,520,471 5 4 33 10 6 40 5 5
Pakistan 2021 43 231,402,117 10 2 27 14 6 34 5 10
Palau 2017 2045 18,024 1 1 3 0 0 2 1 1
Panama 2019 1415 4,351,267 2 0 7 3 0 9 0 1
Paraguay 2009 479 6,703,799 8 2 13 8 3 12 3 8
Peru 2018 412 33,715,472 9 2 29 14 7 33 4 10
Philippines 2022 203 113,880,328 9 3 29 11 5 27 4 10
Poland 2017 1159 38,307,726 9 5 39 15 7 50 5 9
Portugal 2020 2747 10,290,103 0 0 0 0 0 0 0 0
Republic of Korea 2019 3260 51,830,139 2 1 7 8 2 11 2 3
Republic of Moldova 2021 410 3,061,507 10 2 30 14 6 38 4 10
Romania 2021 963 19,328,560 8 2 23 13 6 27 3 10
Russian Federation 2019 936 145,102,755 10 3 40 18 9 46 4 9
Rwanda 2022 60 13,461,888 9 2 24 10 4 26 4 8
Saint Kitts and Nevis 2022 1114 47,607 6 2 17 7 3 14 2 4
Saint Lucia 2022 585 179,652 6 2 17 7 3 14 2 4
Saint Vincent and Grenadines 2022 448 104,332 6 2 17 7 3 14 2 4
Sao Tome and Principe 2022 186 223,108 5 1 12 8 2 13 1 5
Saudi Arabia 2020 1442 35,950,396 10 2 29 16 6 33 5 10
Senegal 2018 71 16,876,720 7 2 22 9 4 15 3 7
Serbia 2022 919 7,296,769 9 4 40 19 7 43 5 10
Seychelles 2022 718 106,471 7 1 11 5 4 8 3 4
Sierra Leone 2021 43 8,420,641 5 1 12 6 2 8 2 3
Slovakia 2023 1685 5,447,622 9 4 37 10 7 38 5 5
Slovenia 2017–2023 2775 2,119,410 11 5 44 19 9 56 6 10
Solomon Islands 2017 106 707,851 3 1 9 6 2 12 2 4
Somalia 2019 33 17,065,581 9 2 23 13 5 27 4 10
South Africa 2020–2021 584 59,392,255 9 2 30 12 6 30 4 9
South Sudan 2018 33 10,748,273 10 2 21 12 5 23 4 10
Spain 2019 3234 47,486,935 0 0 0 0 0 1 0 0
Sri Lanka 2019 166 21,773,441 0 1 1 0 0 0 0 0
Sudan 2014 22 45,657,202 9 3 22 12 3 23 3 10
Suriname 2022 299 612,985 8 2 15 12 4 17 3 6
Sweden 2023 6901 10,467,097 3 1 6 0 1 3 0 1
Syrian Arab Republic 2019 89 21,324,367 9 2 21 12 5 30 3 10
Tajikistan 2009 73 9,750,064 6 1 10 9 1 15 1 9
Thailand 2021 364 71,601,103 9 4 28 14 5 32 5 10
Timor-Leste 2015 135 1,320,942 2 1 3 0 2 4 0 1
Togo 2012 54 8,644,829 7 2 13 7 2 13 2 7
Tonga 2007 279 106,017 1 1 3 0 0 1 0 1
Trinidad & Tobago 2019 1125 1,525,663 11 2 32 14 7 30 4 10
Tunisia 2012 265 12,262,946 5 3 21 8 4 23 4 6
Tuvalu 2010 1071 11,204 1 1 5 1 1 3 0 1
Uganda 2016 43 45,853,778 10 2 21 12 5 22 4 10
Ukraine 2017 368 43,531,422 0 1 5 2 0 5 0 0
United Republic of Tanzania 2021 37 63,588,334 9 2 22 10 5 21 4 10
Uruguay 2020 1620 3,426,260 10 4 38 15 7 41 5 9
Uzbekistan 2021 157 34,081,449 7 2 19 9 7 19 4 7
Vanuatu 2014 133 319,137 0 1 4 1 0 2 0 1
Venezuela (Bolivarian Republic of) 2015 160 28,199,867 9 2 20 12 4 25 4 10
Viet Nam 2018 173 97,468,029 7 1 13 11 2 16 3 10
Yemen 2019 63 32,981,641 10 2 22 12 5 24 2 9
Zambia 2020 75 19,473,125 9 2 16 15 2 22 4 9
Zimbabwe 2020 63 15,993,524 2 1 3 0 0 2 1 1
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Data analysis

To identify changes to the inclusion of cancer treatments in NEMLs, we compared our updated dataset to the 2017 Global Essential Medicines Database [29]. For each cancer medicine, we tabulated the number of countries that listed that medicine in our updated dataset but did not list that medicine in the 2017, and the number of countries that listed that medicine in the 2017 dataset but did not list it in our updated dataset. For our second aim, we conducted our analysis using Stata (16, StataCorp LLC, College Station, TX) for each model, and used multiple linear regression to assess the association between the MIRs and the number of medicines listed in each country's NEML for each cancer (based on the coverage scores). Statistical significance was set at a p-value ≤ 0.05. The co-efficient for each medicine and co-variate, the lower 95% CI (confidence interval), the upper 95% CI, and p-value of the association were recorded. We included healthcare expenditure and population size as they are generally known to influence health systems. The regression was run both unadjusted and adjusted with healthcare expenditure and population. We also ran a model using linear regression to assess the association between MIR and healthcare expenditure for each of the eight cancers.

Role of the funding source

Our funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Results

We collected healthcare expenditure per capita, and population data for all 158 countries with NEMLs for each of the eight cancer causes (see Table 1).

Across the 158 NEMLs, a median of 33 (IQR: 29.5 (19.25–48.75), range: 0–98) cancer treatments were included. The most commonly listed medicines were methotrexate (listed by 145 countries, 92%), medroxyprogesterone (listed by 137 countries, 87%), tamoxifen (listed by 135 countries, 85%), and cyclophosphamide (listed by 131 countries, 83%). Compared with NEMLs from 2017, the most commonly added medicines were docetaxel (added by 46 countries), anastrozole (added by 44 countries), capecitabine (added by 44 countries), oxaliplatin (added by 43 countries), and paclitaxel (added by 40 countries). We also identified 29 cancer medicines that were listed by at least one country in our updated database that were not on any NEML from the 2017 database analysis. These included pembrolizumab (on 17 NEMLs), ibrutinib (on 15 NEMLs), and ruxolitinib (on 15 NEMLs). Of these 29 new medicines, most were not on the WHO Model Lists, except for prembrolizumab, which was added to the 2019 Model List, and ibrutinib, which was added to the 2021 Model List.

We identified 14 medicines for NM skin cancer, 6 for uterine cancer, 55 for breast cancer, 26 for Hodgkin lymphoma, 10 for colon cancer, 73 for leukemia, 6 for cervical cancer, and 10 for testicular cancer. The median medicine coverage scores and IQR for each cancer were: NM skin cancer 8 (IQR 4.75 (4.25–9)), uterine cancer 2 (IQR 1 (1–2)), breast cancer 18 (IQR 17 (10–27)), Hodgkin lymphoma 10 (IQR 7.75 (5.25–13)), colon cancer 3 (IQR 4 (2–6)), leukemia 19 (IQR 17.5 (11.25–28.75), cervical cancer 3 (IQR 3 (1–4), and testicular cancer 8 (IQR 7 (3–10)).

The relationship between variables by regions, with the size of the bubbles representing healthcare expenditure for all eight cancers is shown in Figs. 1 and 2. Figure 1 depicts a decrease in MIR as coverage score increases for non-melanoma skin cancer, uterine cancer, breast cancer, and Hodgkin lymphoma. Figure 2 depicts a decrease in MIR as coverage score increases for colon cancer and leukemia, but not for cervical cancer or testicular cancer.

Fig. 1.

Fig. 1

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Relationship between medicine coverage score and mortality-to-incidence ratio for NM skin cancer, uterine cancer, breast cancer, and Hodgkin’s lymphoma, with the size of the bubbles representing healthcare expenditure

Fig. 2.

Fig. 2

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Relationship between medicine coverage score and mortality-to-incidence ratio for colon cancer, leukemia, cervical cancer, and testicular cancer, with the size of the bubbles representing healthcare expenditure

We present results of two of our regression models in Table 2. Briefly, there were associations between medicine listing and mortality outcomes for six of the eight examined cancers (non-melanoma skin cancer (p = 0.001), uterine cancer (p = 0.006), breast cancer (p = 0.001), Hodgkin lymphoma (p = 0.021), colon cancer (p = 0.006), and leukemia (p = 0.002) when adjusting for healthcare expenditure and population size. For each of the eight cancer causes, the relationship between listing medicines and cancer outcomes are detailed below.

Table 2.

Regression results

MIR with coverage score, adjusted for healthcare expenditure and population size MIR with healthcare expenditure
Beta-Coefficient P-value Lower 95% CI Upper 95% CI Adjusted R2 of model Beta-Coefficient P-value Lower 95% CI Upper 95% CI Adjusted R2 of model
NM Skin −0.0229575 0.001 −0.0360089 −0.0099062 0.2169 −0.000104  < 0.0001 −0.0001404 −0.0000677 0.1647
Uterine −0.019615 0.006 −0.0334501 −0.0057798 0.2989 −0.00005  < 0.0001 −0.0000629 −0.0000372 0.2701
Breast −0.0032446 0.001 −0.0051592 0.0013301 0.3983 −0.0000839  < 0.0001 −0.0001015 −0.0000663 0.3591
Hodgkin −0.0070121 0.021 −0.129642 0.0010599 0.4065 −0.0000839  < 0.0001 −0.0001015 −0.0000663 0.3591
Colon −0.0117881 0.006 −0.0200667 −0.0035094 0.4773 −0.0000958  < 0.0001 −0.0001124 −0.0000793 0.4528
Leukemia −0.001513 0.002 −0.0024832 −0.0005429 0.5387 −0.000678  < 0.0001 −0.0000782 −0.0000574 0.5123
Cervical −0.0051497 0.214 −0.0133043 0.0030049 0.4319 −0.0000607  < 0.0001 −0.0000716 −0.0000498 0.4334
Testicular −0.0044925 0.145 −0.0105539 0.0015688 0.2674 −0.0000648  < 0.0001 −0.0000818 −0.0000477 0.2604
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Non-melanoma skin cancer

The unadjusted model demonstrated that listing the medicines for non-melanoma skin cancer was negatively associated with the mortality to incidence ratio (b = −0.016 indicating that for each additional cancer medicine listed the MIR decreased by 0.016, p = 0.024). The proportion of variance in the MIR explained by a country listing non-melanoma skin cancer medicines was 2.6% (adjusted R2 = 0.026). After adjusting for healthcare expenditure and population size, we found a negative association between listing non-melanoma skin cancer medicines and MIR (b = −0.023, p = 0.001). In this adjusted model, the number of medicines listed on NEMLs along with healthcare expenditure and population size accounted for 21.7% of the differences in non-melanoma cancer MIR (adjusted R2 = 0.217).

Uterine cancer

In both the unadjusted and adjusted models, listing uterine cancer medicines was negatively associated with countries’ MIRs (b = −0.027, p = 0.001; and b = −0.020, p = 0.006, respectively). The number of medicines explained 6.1% of the variance in the MIR (adjusted R2 = 0.061). The number of medicines, healthcare expenditure and population size explained 29.9% of the variance in the MIR (adjusted R2 = 0.299).

Breast cancer

In the unadjusted model, listing breast cancer medicines was negatively associated with countries’ breast cancer mortality-to-incidence ratios (b = −0.004, p = 0.001). The number of listed medicines explained 5.7% of the variance in the MIR (adjusted R2 = 0.057). The adjusted model also demonstrated a significant association between listing breast cancer medicines and the countries’ breast cancer MIRs (b = −0.003, p = 0.001). The adjusted model accounted for 39.8% of the variance in the MIR (adjusted R2 = 0.398).

Hodgkin lymphoma

For Hodgkin lymphoma, listing cancer medicines was not significantly associated with the countries’ MIRs in the unadjusted model (b = −0.004, p = 0.356). The unadjusted regression model showed that listing Hodgkin lymphoma medicines accounted for little of the variance in MIR (adjusted R2 = −0.0009). In adjusted model, listing Hodgkin lymphoma medicines was negatively associated with MIR (b = −0.007, p = 0.021). The adjusted model accounted for 40.6% of the variance in the MIR (adjusted R2 = 0.406).

Colon cancer

The unadjusted model demonstrated that listing the medicines for colon cancer was negatively associated with countries’ colon cancer MIRs (b = −0.012, p = 0.039). Listing colon cancer medicines accounted for 2.1% of the variance in countries MIRs (adjusted R2 = 0.021). The adjusted model showed a significant association between listing colon cancer medicines and countries’ colon cancer MIRs (b = −0.012, p = 0.006). The adjusted model accounted for 47.7% of the variance in countries’ MIRs (adjusted R2 = 0.477).

Leukemia

The unadjusted model demonstrated that listing the medicines for leukemia was negatively associated with countries’ colon cancer MIRs (b = −0.002, p = 0.003). For the unadjusted model, 5.1% of the variance in countries’ leukemia MIRs were explained by listing leukemia medicines (adjusted R2 = 0.051). In the adjusted model, there was also a significant association between listing leukemia medicines and countries’ leukemia MIRs (b = −0.002, p = 0.002). For the adjusted model, 53.9% of the variance in countries’ leukemia MIRs were explained by listing leukemia medicines, healthcare expenditure, and population size (adjusted R2 = 0.539).

Cervical cancer

For cervical cancer, listing cancer medicines was not significantly associated with cervical cancer MIR in both the unadjusted (b = −0.001, p = 0.785) and adjusted (b = −0.005, p = 0.214) models. The unadjusted regression model showed that listing medicines for cervical cancer accounted for little of the variance in MIR (adjusted R2 = −0.006), and the adjusted model showed that listing medicines for cervical cancer, as well as healthcare expenditure and population size accounted for 43.2% of the variance in MIR (adjusted R2 = 0.432).

Testicular cancer

For testicular cancer, listing testicular cancer medicines was not significantly associated with testicular cancer MIR in both the unadjusted (b = −0.003, p = 0.930) and adjusted (b = −0.004, p = 0.145) models. The unadjusted regression model showed that listing medicines for testicular cancer accounted for little of the variance in MIR (adjusted R2 = −0.006), and the adjusted model accounted for 26.7% of the variance in MIR (adjusted R2 = 0.267).

Discussion

Listing essential medicines for NM skin cancer, uterine cancer, breast cancer, Hodgkin lymphoma, colon cancer, and leukemia was associated with better disease-specific avoidable mortality when controlling for healthcare expenditure and population size, but this relationship was not present for cervical cancer and testicular cancer. The most commonly added medicines were docetaxel, anastrozole, capecitabine, oxaliplatin, and paclitaxel, and some new cancer medicines were listed that were not listed in previously in any NEML.

Our findings align with our previous study that found only a weak association between listing cancer treatments and relevant cancer outcomes [13]. These results could be explained by the listed medicines not being available or accessible, by aspects of cancer care outside of medicine access, or by limits of the benefits of cancer treatments. While including cancer medicines on NEMLs is necessary to establish government priorities and standards, these priorities do not always translate into access for patients. Essential cancer medicines may not always be accessible to those who need them, and there are barriers to availability and affordable access to cancer medicines. A 2017 study of 63 countries found that that many of the cancer medicines listed in the WHO EML were either not available at all, or only available at full cost, particularly in low-income and low-middle-income countries [30]. Several countries have reported low availability of cancer medicines as well as lack of affordability [31–41]. Some high-income countries also face lack of availability and affordability of cancer medicines [30, 39, 42–44]. Poor cancer medicine availability can result in treatment changes and delays that may impact medicine efficacy and toxicity [44–47]. Some patients may forgo cancer medicines altogether [31]. Furthermore, NEMLs do not always align with other national policies and systems, such as procurement and distribution, and drug reimbursement and coverage policies [48, 49].

Cancer medicines represent only one subset of cancer care, and other aspects of the cancer care continuum contribute to cancer outcomes. Improved prevention, screening, and diagnosis programs have contributed to greater reductions in cancer mortality in high-income countries compared to lower-income countries [50, 51]. Other treatment modalities such as radiation therapy also play a role in cancer outcomes [51, 52]. Many countries lack sufficient radiotherapy infrastructure, due to the high upfront costs of obtaining the machines [51, 53].

In particular, the lack of an association seen with testicular cancer may be explained by the greater role of surgical interventions in testicular cancer treatment [54]. For cervical cancer, which also did not show an association with cancer medicine listing, screening may play a role in mortality [55]. Notably, for all eight cancers, there is a large difference between the variance in the unadjusted and adjusted models, indicating that adjusting for population size and healthcare expenditure has an important influence. This aligns with findings that demonstrate associations between healthcare expenditures and cancer mortality [56, 57].

Our findings indicate a weak relationship between listing cancer treatments and cancer outcomes; most NEMLs list established cancer medicines, and this raises questions about whether listing newer medicines would greatly reduce cancer mortality. Evidence supporting the treatment benefits of novel cancer medicines is not always robust, and some studies of newer cancer treatments have a high risk of bias [58–60]. From 2018 to 2024, 85% of cancer medicine launches had annual costs above $100,000 USD, and 55% of new cancer medicines launches had annual costs above $200,000 USD [61]. However, it has been reported that there is no meaningful association between cancer medicines prices and the magnitude of benefit [62]. When considering adding newer cancer treatments to NEMLs, treatment benefits, as well as financial costs and affordability should be taken into account.

Strengths and limitations

A major strength of this study is that we included a large number of countries; we identified 158 countries with NEMLs and obtained disease-specific MIRs, healthcare expenditure, and population size data for all 158 countries. However, NEMLs for other countries may exist but may not have been identified in our searches. Furthermore, we used a robust search method to identify clinically appropriate medicines for each of the eight cancers, using WHO guidelines, MEDI-HPS and MIA databases, and the WHO 2023 EML. Finally, we used MIR as our outcome variable, which is often used as a measure of cancer outcomes since it is based on mortality and incidence data that is readily available in several countries. However, this measure relies on mortality and incidence being accurately recorded and may be affected by non-treatment related factors such as screening and diagnosis, and other cancer treatments such as radiation and surgery may affect cancer outcomes. Furthermore, there are variations in how countries use NEMLs, and many countries face barriers in implementing NEMLs, which can impact medicine access [48]. There is limited available information about how NEMLs are utilized and implemented in different countries, which can impact the robustness of our findings.

Implications for policy and research

Our findings indicate that simply listing cancer medicines on NEMLs play a small role in reducing cancer mortality. Policymakers should consider the importance of improving equitable access to cancer treatments, which requires investments in healthcare capacity and resourcing at local, national, and international levels. As well, our findings highlight the need to address cancer care through several modalities, including screening and diagnosis, indicating the need for continued investments in preventative care programs that reach all populations, including people who experience disadvantages. Further research could include country-specific analyses to capture the effects of listing cancer medicines while accounting for how the NEML is utilized, and how medicines are made available to the population. Future research could also examine the cost of cancer medicines in relation to their inclusion on NEMLs and effects on cancer mortality.

Conclusion

There was an association between listing cancer medicines on NEMLs and cancer mortality. Further research is required to explore how cancer mortality may be impacted by other cancer interventions (such as screening, diagnosis, surgery, and radiation), and policies to improve equitable access to cancer care.

Supplementary Information

Supplementary Material 1. (30KB, docx)

Acknowledgements

Not applicable.

Abbreviations

EML

Essential medicines list

GEM

Global essential medicines

NEML

National essential medicines list

WHO

World Health Organization

Authors' contributions

MU, CH, AR, and AW collected and curated the data. AB and MU and conducted data analysis. MU critically reviewed and revised the manuscript for important intellectual content. NP conceptualized and designed the study, coordinated and supervised data collection, drafted the initial manuscript, and critically reviewed and revised the manuscript.

Funding

The work was completed with funding from the Canada Research Chairs program and funding from the World Health Organization.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J Clinicians. 2021;71(3):209–49. [DOI] [PubMed] [Google Scholar]
  • 2.World Health Organization Model List of Essential Medicines – 23rd List, 2023. World Health Organization; 2023. Available from: https://www.who.int/publications/i/item/WHO-MHP-HPS-EML-2023.02.
  • 3.Jenei K, Aziz Z, Booth C, Cappello B, Ceppi F, De Vries EGE, et al. Cancer medicines on the WHO Model List of Essential Medicines: processes, challenges, and a way forward. Lancet Glob Health. 2022;10(12):e1860–6. [DOI] [PubMed] [Google Scholar]
  • 4.Selection of Essential Medicines At Country Level: Using the WHO Model List of Essential Medicines to update a national essential medicines list. Geneva: World Health Organization; 2020. p. 48. https://iris.who.int/bitstream/handle/10665/330898/9789241515443-eng.pdf?sequence=1.
  • 5.Bazargani YT, de Boer A, Schellens JHM, Leufkens HGM, Mantel-Teeuwisse AK. Selection of oncology medicines in low- and middle-income countries. Ann Oncol. 2014;25(1):270–6. [DOI] [PubMed] [Google Scholar]
  • 6.Bazargani YT, Ewen M, De Boer A, Leufkens HGM, Mantel-Teeuwisse AK. Essential Medicines Are More Available than Other Medicines around the Globe. Choonara I, editor. PLoS ONE. 2014;9(2):e87576. [DOI] [PMC free article] [PubMed]
  • 7.Aitken M. Understanding the Role and Use of Essential Medicines Lists. IMS Institute for Healthcare Informatics; 2015. Available from: https://www.farmaindustria.es/web_en/wp-content/uploads/sites/3/2015/04/IIHI_Essential_Medicines_Report_2015-Web3.pdf.
  • 8.Fundytus A, Wells JC, Sharma S, Hopman WM, Del Paggio JC, Gyawali B, et al. Industry Funding of Oncology Randomised Controlled Trials: Implications for Design. Results and Interpretation Clin Oncol (R Coll Radiol). 2022;34(1):28–35. [DOI] [PubMed] [Google Scholar]
  • 9.Del Paggio JC, Berry JS, Hopman WM, Eisenhauer EA, Prasad V, Gyawali B, et al. Evolution of the Randomized Clinical Trial in the Era of Precision Oncology. JAMA Oncol. 2021;7(5):728–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Paggio JCD, Sullivan R, Booth CM. Targeting the value of targeted therapy. Oncotarget. 2017;8(53):90612–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Booth CM, Sengar M, Goodman A, Wilson B, Aggarwal A, Berry S, et al. Common Sense Oncology: outcomes that matter. Lancet Oncol. 2023;24(8):833–5. [DOI] [PubMed] [Google Scholar]
  • 12.Gyawali B, Sullivan R, Booth CM. Cancer groundshot: going global before going to the moon. Lancet Oncol. 2018;19(3):288–90. [DOI] [PubMed] [Google Scholar]
  • 13.Ikpeni O (Theresa), Maraj D, Woods H, Workentin A, Booth CM, Persaud N. Essential cancer medicines and cancer outcomes: Cross‐sectional study of 124 countries. Cancer Med. 2023;12(22):20745–58. [DOI] [PMC free article] [PubMed]
  • 14.Haakenstad A, Yearwood JA, Fullman N, Bintz C, Bienhoff K, Weaver MR, et al. Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Glob Health. 2022;10(12):e1715–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Parkin DM, Bray F. Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. Eur J Cancer. 2009;45(5):756–64. [DOI] [PubMed] [Google Scholar]
  • 16.World Health Organization. Global Breast Cancer Initiative Implementation Framework: assessing, strengthening and scaling-up of services for the early detection and management of breast cancer. Geneva: World Health Organization; 2023. Available from: https://www.who.int/publications/i/item/9789240065987. [cited 2023 Dec 7].
  • 17.World Health Organization Regional Office for the Eastern Mediterranean. Guidelines for management of breast cancer. 2006. Available from: https://iris.who.int/handle/10665/119806. [cited 2023 Dec 7].
  • 18.World Health Organization Regional Office for the Western Pacific. Strategic framework for the comprehensive prevention and control of cervical cancer in the Western Pacific Region 2023–2030. Manila: World Health Organization Regional Office for the Western Pacific; p. 2023. Available from: https://www.who.int/publications/i/item/9789290620068. [cited 2023 Dec 7].
  • 19.World Health Organization, Regional Office for South-East Asia. Regional implementation framework for elimination of cervical cancer as a public health problem: 2021–2030. New Delhi: World Health Organization, Regional Office for South-East Asia; 2021. Available from: https://www.who.int/publications/i/item/9789290228875. [cited 2023 Dec 7].
  • 20.World Health Organization. Comprehensive cervical cancer control: a guide to essential practice - 2nd edition. Geneva: World Health Organization; 2014. Available from: https://www.who.int/publications-detail-redirect/9789241548953. [cited 2023 Dec 7]. [PubMed]
  • 21.World Health Organization. Tackling NCDs: “Best Buys” and Other Recommended Interventions for the Prevention and Control of Noncommunicable Diseases. World Health Organization; 2017. Available from: https://apps.who.int/iris/handle/10665/259232. [cited 2023 Dec 7].
  • 22.Wei-Qi We, Joshua Denny. MEDication Indication high precision subset. Vanderbilt University Medical Center; 2013. Available from: https://www.vumc.org/cpm/cpm-blog/medi-ensemble-medication-indication-resource-0#:~:text=The%20MEDI%20high%20precision%20subset,HPS%20is%206.22%C2%B16.09.
  • 23.Wei-Qi We, Joshua Denny. MEDication Indication high precision subset. Vanderbilt University Medical Center; 2020. Available from: https://www.vumc.org/wei-lab/medi.
  • 24.MIA: Mapping ICD (International Classification of Diseases) Disease Codes with ATC (Anatomical Therapeutic Chemical) Drug Codes. HuLab; Available from: hulab.rxnfinder.org/mia.
  • 25.World Health Organization. Global Health Observatory (GHO) data. 2023. Available from: https://www.who.int/data/gho.
  • 26.Press Release: North Korea’s crumbling health system in dire need of aid. Amnesty International; 2010. Available from: https://www.amnesty.org/fr/wp-content/uploads/2021/07/pre012242010en.pdf.
  • 27.Brink S. What Country Spends The Most (And Least) On Health Care Per Person? NPR. 2017; Available from: https://www.npr.org/sections/goatsandsoda/2017/04/20/524774195/what-country-spends-the-most-and-least-on-health-care-per-person. [cited 2024 Feb 28].
  • 28.CIA World Factbook 2023. Washington, DC: Central Intelligence Agency; 2023. Available from: https://www.cia.gov/the-world-factbook/.
  • 29.Persaud N, Jiang M, Shaikh R, Bali A, Oronsaye E, Woods H, et al. Comparison of essential medicines lists in 137 countries. Bull World Health Organ. 2019;97(6):394–404C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cherny NI, Sullivan R, Torode J, Saar M, Eniu A. ESMO International Consortium Study on the availability, out-of-pocket costs and accessibility of antineoplastic medicines in countries outside of Europe. Ann Oncol. 2017;28(11):2633–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rurangwa C, Ndayisenga J, Sezirahiga J, Nyirimigabo E. Availability and affordability of anticancer medicines at cancer treating hospitals in Rwanda. BMC Health Serv Res. 2023;23(1):717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ocran Mattila P, Biritwum RB, Babar ZUD. A comprehensive survey of cancer medicines prices, availability and affordability in Ghana. Limwattananon C, editor. PLoS ONE. 2023;18(5):e0279817. [DOI] [PMC free article] [PubMed]
  • 33.Prinja S, Bahuguna P, Tripathy JP, Kumar R. Availability of medicines in public sector health facilities of two North Indian States. BMC Pharmacol Toxicol. 2015;16(1):43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Alemu BA, Hailemariam FH. Price, Availability and Affordability of Anti-Cancer Medicines in Addis Ababa. Ethiopia RMHP. 2022;15:2421–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Sarwar MR, Iftikhar S, Saqib A. Availability of anticancer medicines in public and private sectors, and their affordability by low, middle and high-income class patients in Pakistan. BMC Cancer. 2018;18(1):14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Petricca K, Kambugu J, Githang’a J, Macharia WM, Njuguna F, McLigeyo A, et al. Access to essential cancer medicines for children: a comparative mixed-methods analysis of availability, price, and health-system determinants in east Africa. Lancet Oncol. 2023;24(5):563–76. [DOI] [PubMed]
  • 37.Kizub DA, Naik S, Abogan AA, Pain D, Sammut S, Shulman LN, et al. Access to and Affordability of World Health Organization Essential Medicines for Cancer in Sub-Saharan Africa: Examples from Kenya, Rwanda, and Uganda. Oncologist. 2022;27(11):958–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Varmaghani M, Elyasi S, Mojahedian MM, Ghavami V, Borhani B, Javan-Noughabi J, et al. Availability and affordability of anticancer medicines in Iran based on WHO/HAI standard survey methods. Support Care Cancer. 2023;31(1):89. [DOI] [PubMed] [Google Scholar]
  • 39.Denburg AE, Fundytus A, Khan MS, Howard SC, Antillon-Klussmann F, Sengar M, et al. Defining Essential Childhood Cancer Medicines to Inform Prioritization and Access: Results From an International, Cross-Sectional Survey. JCO Glob Oncol. 2022;(8):e2200034. [DOI] [PMC free article] [PubMed]
  • 40.Das M. Economic crisis in Sri Lanka causing cancer drug shortage. Lancet Oncol. 2022;23(6):710. [DOI] [PubMed] [Google Scholar]
  • 41.Martei YM, Chiyapo S, Grover S, Ramogola-Masire D, Dryden-Peterson S, Shulman LN, et al. Availability of WHO Essential Medicines for Cancer Treatment in Botswana. JGO. 2018;4:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Moye-Holz D, Vogler S. Comparison of Prices and Affordability of Cancer Medicines in 16 Countries in Europe and Latin America. Appl Health Econ Health Policy. 2022;20(1):67–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Cherny N, Sullivan R, Torode J, Saar M, Eniu A. ESMO European Consortium Study on the availability, out-of-pocket costs and accessibility of antineoplastic medicines in Europe. Ann Oncol. 2016;27(8):1423–43. [DOI] [PubMed] [Google Scholar]
  • 44.Jenei K, Lythgoe MP, Vokinger KN. Shortages of essential cancer medicines: who is responsible and what are the possible solutions? Lancet Oncol. 2024;25(1):23–6. [DOI] [PubMed] [Google Scholar]
  • 45.Chabner BA. Drug Shortages — A Critical Challenge for the Generic-Drug Market. N Engl J Med. 2011;365(23):2147–9. [DOI] [PubMed] [Google Scholar]
  • 46.Becker DJ, Talwar S, Levy BP, Thorn M, Roitman J, Blum RH, et al. Impact of Oncology Drug Shortages on Patient Therapy: Unplanned Treatment Changes. JOP. 2013;9(4):e122–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Cherif Chefchaouni A, Moutaouakkil Y, Adouani B, Tadlaoui Y, Lamsaouri J, Bousliman Y. Impact of anti-cancer drugs shortages in oncology and hematology departments in a Moroccan hospital. J Oncol Pharm Pract. 2022;28(4):822–6. [DOI] [PubMed] [Google Scholar]
  • 48.Peacocke EF, Myhre SL, Foss HS, Gopinathan U. National adaptation and implementation of WHO Model List of Essential Medicines: A qualitative evidence synthesis. Kesselheim AS, editor. PLoS Med. 2022;19(3):e1003944. [DOI] [PMC free article] [PubMed]
  • 49.Nyanchoka M, Mulaku M, Nyagol B, Owino EJ, Kariuki S, Ochodo E. Implementing essential diagnostics-learning from essential medicines: A scoping review. Eboreime E, editor. PLOS Glob Public Health. 2022;2(12):e0000827. [DOI] [PMC free article] [PubMed]
  • 50.World Health Organization. WHO report on cancer: setting priorities, investing wisely and providing care for all. Geneva; 2020. Available from: https://www.who.int/publications/i/item/9789240001299.
  • 51.Batouli A, Jahanshahi P, Gross CP, Makarov DV, Yu JB. The global cancer divide: Relationships between national healthcare resources and cancer outcomes in high-income vs. middle- and low-income countries. JEGH. 2013;4(2):115. [DOI] [PMC free article] [PubMed]
  • 52.Medenwald D, Vordermark D, Dietzel C. Number of radiotherapy treatment machines in the population and cancer mortality: an ecological study. CLEP. 2018;10:1249–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Zubizarreta EH, Fidarova E, Healy B, Rosenblatt E. Need for Radiotherapy in Low and Middle Income Countries – The Silent Crisis Continues. Clin Oncol. 2015;27(2):107–14. [DOI] [PubMed] [Google Scholar]
  • 54.Baird DC, Meyers GJ, Hu JS. Testicular Cancer: Diagnosis and Treatment. Am Fam Physician. 2018;97(4):261–8. [PubMed] [Google Scholar]
  • 55.Simms KT, Keane A, Nguyen DTN, Caruana M, Hall MT, Lui G, et al. Benefits, harms and cost-effectiveness of cervical screening, triage and treatment strategies for women in the general population. Nat Med. 2023;29(12):3050–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Sung WW, Au KK, Wu HR, Yu CY, Wang YC. Improved trends of lung cancer mortality-to-incidence ratios in countries with high healthcare expenditure. Thorac Cancer. 2021;12(11):1656–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Chow RD, Bradley EH, Gross CP. Comparison of Cancer-Related Spending and Mortality Rates in the US vs 21 High-Income Countries. JAMA Health Forum. 2022;3(5):e221229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Naci H, Davis C, Savović J, Higgins JPT, Sterne JAC, Gyawali B, et al. Design characteristics, risk of bias, and reporting of randomised controlled trials supporting approvals of cancer drugs by European Medicines Agency, 2014–16: cross sectional analysis. BMJ. 2019;366:l5221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Michaeli DT, Michaeli T. Overall Survival, Progression-Free Survival, and Tumor Response Benefit Supporting Initial US Food and Drug Administration Approval and Indication Extension of New Cancer Drugs, 2003–2021. JCO. 2022;40(35):4095–106. [DOI] [PubMed] [Google Scholar]
  • 60.Tannock IF, Pond GR, Booth CM. Biased Evaluation in Cancer Drug Trials—How Use of Progression-Free Survival as the Primary End Point Can Mislead. JAMA Oncol. 2022;8(5):679. [DOI] [PubMed] [Google Scholar]
  • 61.IQVIA Institute for Human Data Science. Global Oncology Trends 2024: Outlook to 2028. 2024. Available from: https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/global-oncology-trends-2024#:~:text=Cancer%20medicine%20spending%20rose%20to,over%20the%20last%20five%20years.
  • 62.Miljković MD, Tuia JE, Olivier T, Haslam A, Prasad V. Association Between US Drug Price and Measures of Efficacy for Oncology Drugs Approved by the US Food and Drug Administration From 2015 to 2020. JAMA Intern Med. 2022;182(12):1319. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (30KB, docx)

Data Availability Statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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