
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12867-8
10.1016/j.heliyon.2024.e36836
e36836
Research Article
Modelling incidence and mortality cancer parameters with respect to GLOBOCAN 2020Age standardized world estimates
Acquah Joseph a
Bosson-Amedenu Senyefia b
Eyiah-Bediako Francis c
Buabeng Albert abuabeng@umat.edu.gh
a⁎
Ouerfelli Noureddine d
a Mathematical Sciences Department, University of Mines and Technology, Tarkwa, Ghana
b Department of Mathematics, Statistics and Actuarial Science, Takoradi Technical University, Takoradi, Ghana
c Department of Statistics, University of Cape Coast, Cape Coast, Ghana
d Institut Supérieur des Technologies Médicales de Tunis, LR13SE07, Laboratoire de Biophysique et Technologies Médicales, Université de Tunis El Manar, Tunis, Tunisia
⁎ Corresponding author. Mathematical Sciences Department, University of Mines and Technology, Tarkwa, Ghana. abuabeng@umat.edu.gh
25 8 2024
15 9 2024
25 8 2024
10 17 e368366 6 2023
31 7 2024
22 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
In this paper, an empirical model with the critical number of incidences and deaths for female cancers as adjustable parameters has been developed using life expectancy data from GLOBOCAN world estimates on cancer types. The model was developed based on the cumulative risk and exponential correlation techniques such that the significance of the adjustable parameters ascertains the strength of this research in two ways. First, it indicates indicates the rapid increase in female cancer morbidity with increase in the number of male cancer cases, regardless of incidence or mortality. Second, it suggests that female cancer cases may approach a virtual limiting value as male cancer cases reach extremely high levels. This projection aligns with the global population trends, indicating a proportional increase in cancer cases each year. Additionally, the cumulative risk of cancer incidences of each sex has been modelled separately against the global cumulative risk of cancer incidences of both sexes which revealed regions that have passed their inflection point and those that are yet to reach the inflection point. There was a curvature change, which indicates an inflection point coinciding with the South-Eastern Region and indicating that for the regions beyond the inflection point, the increase of the cumulative risk of cancer incidences of females against that of males is more accentuated compared with the regions before the inflection point. However, when the cumulative risk of cancer mortality of each sex is modelled separately against the global cumulative risk of cancer mortality of both sexes shows a non-linear dependence and the increase is more accentuated for males than for females. This finding indicates that cumulative risk is influenced by factors beyond the male-female population ratio. This study advances cancer studies as it provides a nuanced understanding of gender-specific cancer trends, crucial for developing targeted cancer prevention and treatment strategies. By elucidating the dynamics of cancer incidences and mortalities across different regions and sexes, the findings can inform public health policies and resource allocation to combat cancer more effectively on a global scale.

Keywords

Cancer
GLOBOCAN
Cumulative incidence
Mortality
Age Standardised
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pmc1 Introduction

A strong foundation for estimating the global cancer burden was lacking until recently due to insufficient data on the global distribution of cancer in specific communities and countries. Certain types of tumors, such as colorectal, prostate, and breast cancer, previously had high incidence rates primarily in Western Europe, North America, and Australia. However, these rates are now increasing in many other countries. While lung cancer was initially believed to be prevalent only in high-income countries due to its high frequency, it is now recognized as a global disease. Previously, stomach, liver, and cervical cancers were more prevalent in low-income nations, but changes in the prevalence of these cancer types over time indicate national differences. There are significant variations in cancer death rates among different countries or regions, with a growing burden in countries with low or middle incomes due to inadequate implementation of preventive therapies and late-stage diagnosis rather than early-stage cancer detection. In 2020, the cumulative risk of African women dying from cancer is comparable to that of women in the wealthiest countries of Europe and North America [1,2].

In terms of disease mortality, lung cancer is more common than breast cancer in Australia, Scandinavia, North America, and China. Cervical cancer is more prevalent than breast cancer in several Sub-Saharan African nations. The highest mortality rates globally are currently in Sub-Saharan Africa, an area with poor survival rates due to inadequate health infrastructure. These rates have risen concurrently with the region. The 5-year age-standardized relative survival rate for patients diagnosed between 2008 and 2015 in 12 Sub-Saharan African countries was 66 %, while in high-income countries between 2010 and 2014, it ranged from 85 % to 90 % [3,4].

The global burden of cancer morbidity and mortality is increasing rapidly, primarily due to changes in the distribution and prevalence of major cancer risk factors, many of which are associated with socioeconomic development. Additionally, factors such as population aging and growth contribute to this rise. The impact of cancer as a cause of premature death has been compared to the levels of social and economic development in different countries. With Asia being home to 59.5 % of the world's population, it is not surprising that half of all cancer diagnoses and 58.3 % of cancer-related deaths for both genders occurred there in 2020. Europe accounts for 22.8 % of all cancer cases and 19.6 % of cancer deaths, despite representing only 9.7 % of the global population. The Americas follow with 20.9 % of cases and 14.2 % of deaths. The proportion of cancer-related deaths in Asia (58.3 %) and Africa (7.2 %) is higher than their share of cancer incidence (49.3 % and 5.7 %, respectively), due to the varied distribution of cancer types and higher fatality rates in these regions.

In terms of incidence and mortality, lung and colorectal cancers come in second and third, respectively, after breast cancer, which is the most common disease in women and the leading cause of cancer-related deaths. The most common cancers diagnosed in women are lung (11.4 %), colorectal (10.0 %), prostate (7.3 %), and stomach (5.6 %) cancers (11.7 % of total cases). With 18.0 % of all cancer-related deaths, lung cancer is the most frequent type of cancer. Colorectal (9.4 %), liver (8.3 %), stomach (7.7 %), and female breast (6.9 %) cancers are the next most common. Men die from cancer most frequently from lung cancer, which is followed in terms of incidence by prostate and colorectal cancer and mortality by liver and colorectal cancer [2,5]. The primary cancer types vary greatly throughout the world, especially concerning the occurrence in men (8 types) and the death rates in men (8 types) and women (7 types). In 112 countries, the most frequent cancer among men is prostate cancer. Lung cancer is next in 36, colorectal cancer is in 36, and liver cancer is in 11 countries. Because of its high fatality rate of 21, lung cancer ranks first among cancer-related deaths among men in 93 countries. The next most common cancers that kill men (23 countries) are liver cancer (48 countries) and prostate cancer (48 countries). Compared to men, the most prevalent cancers diagnosed in women are breast cancer (159 countries) and cervical cancer (23 of 26 countries). Additionally, the most prevalent diseases diagnosed in women are breast cancer (159 countries) and cervical cancer (23 of the remaining 26 countries). Lung cancer is the main cause of cancer death in 25 countries, whereas breast and cervical cancer rank first in 110 and 36 countries, respectively. The incidence rates for both men and women tend to rise as the Human Development Index (HDI) rises; in low-HDI countries, they range from 104.3 to 128.0 per 100,000 to 335.3 and 267.6 per 100,000 in extremely high HDI countries. While women's mortality rates vary little between HDI levels (67.0–88.4 per 100,000), men's mortality rates were approximately twice as high in higher HDI nations (122.9–141.1 per 100,000) as in lower HDI countries (76.7–78.0 per 100,000) [2,5].

Men's overall cancer incidence rates in 2020 were 19 % higher than women's, at 222.0 per 100,000; however, regional rates differed significantly. While rates for women varied nearly fourfold, from 405.2 per 100,000 in Australia/New Zealand to 102.5 per 100,000 in South Central Asia, men's rates varied nearly fivefold, from 494.2 per 100,000 in Australia to 100.6 per 100,000 in Western Africa. These discrepancies are mostly caused by variations in the cancer mix, or the combination of risk factors and related malignancies, as well as barriers to excellent cancer prevention and early detection. Males die from cancer at a rate 43 % greater than females, with overall cancer mortality rates twice as high in men as in women (120.8 and 84.2 per 100,000, respectively). Women's mortality rates varied from 118.3 per 100,000 in Melanesia to 63.1 per 100,000 in Central America and South Central Asia, while men's death rates ranged from 165.6 per 100,000 in Eastern Europe to 70.2 per 100,000 in Central America. Compared to Northern America (8.2 %), Western Europe (8.8 %), Australia/New Zealand (7.4 %), and Eastern Africa (11.0 %), women in Eastern Africa had a higher cumulative probability of dying from cancer in 2020 [2,5].

Owing to the recent disparities and variabilities in cancer type distribution, this study proposes an empirical model with the critical number of incidences and deaths for cancers as adjustable parameters. Life expectancy data from GLOBOCAN world estimates on cancer types are used for the estimation of relevant model parameters. This study advances the field of cancer modelling with the introduction of these adjustable parameters that play two significant roles. First, it indicates indicates the rapid increase in female cancer morbidity with increase in the number of male cancer cases, regardless of incidence or mortality. Second, it implies that the number of female cases can reach a kind of virtual limiting value when the number of male cases reaches extremely high levels.

1.1 Advantages and Novelty of the study

This study presents several advantages and novel contributions to the field of biomedical research.• Comprehensive Risk Assessment: By modelling the cumulative risk of cancer incidences and mortalities, the approach used in this study provides a detailed assessment of cancer trends that can inform public health policies and resource allocation.

• Regional Insights: The model identifies regions that have surpassed their inflection points in cancer incidences, offering critical insights into regional disparities and areas requiring targeted interventions.

• Predictive Power: The use of exponential correlation techniques enhances the model's ability to predict future cancer trends, which is valuable for planning and prevention efforts.

1.2 Relevance to biomedical applications

The results of this study have significant consequences for biomedical applications.• Health Policy Strategies: The developed model can guide the development of targeted cancer prevention and treatment strategies, improving public health outcomes.

• Resource Allocation: By identifying high-risk regions and populations, this study supports efficient resource allocation for cancer care and prevention programs.

• Early Detection and Intervention: The predictive capabilities of the developed model can assist in the timely identification and management of cancer, potentially reducing mortality rates.

• Comparison with Existing Therapeutics: The developed model offers a complementary approach to existing cancer therapeutics by providing a population-level perspective on cancer trends. While it does not directly address therapeutic interventions, it highlights areas where improved therapeutic strategies are needed and can enhance the effectiveness of existing treatments by identifying high-risk groups.

2 Global interpretation on world cancers by regions

Of the 183 countries studied, 134 have cancer as the primary or secondary cause of premature mortality (deaths occurring between ages 30 and 69), while in 45 additional countries, cancer ranks as the third or fourth leading cause. In nations with high or very high HDI scores, cancer is the predominant cause of premature death. These include most European countries (such as France, Germany, and the United Kingdom), Australia and New Zealand in Oceania, Japan, the Republic of Korea, and Singapore in Asia, Argentina and Chile in South America, and the United States and Canada in North America. Additionally, in Vietnam and Thailand, cancer is the leading cause of death. After cardiovascular diseases, cancer is the second leading cause of mortality in Brazil, China, several Eastern European countries (including the Russian Federation and Ukraine), as well as Algeria and Egypt [6].

In most Sub-Saharan African countries, cancer ranks third or fourth, with only a few nations placing it fifth or sixth. Low-income countries experience higher rates of poverty-related non-communicable diseases (NCDs) such as respiratory illnesses linked to poor living conditions, infection-related cancers (including stomach, liver, and cervical cancers), and cardiovascular diseases caused by malnutrition during fetal and childhood development [6].

2.1 Brief overview of global incidence and mortality trends for major cancer types

Australia, Japan, the United Kingdom, and the United States have all experienced increases in lung cancer rates. However, the United States and the United Kingdom have seen the most significant rises followed by subsequent declines. While the incidence and mortality rates of lung cancer in men vary by country, they are almost always linked to tobacco use from 20 to 30 years prior. Correspondingly, the rates indicate that women's smoking epidemics often started later or not at all in many countries. Lung cancer is the most prevalent cancer worldwide in terms of both incidence, with 2.1 million new cases in 2018, and mortality, with 1.8 million deaths in the same year. The primary cause of lung cancer is tobacco use, which is responsible for 63 % of lung cancer deaths worldwide and more than 90 % of lung cancer deaths in countries where both men and women smoke [7,8].

With 2.1 million new cases recorded in 2018, breast cancer is the most common cancer among women. It also accounts for 627,000 of all female cancer-related deaths in 2018 [5]. According to research published in The Lancet Oncology, breast cancer represented approximately 24.2 % of all cancer cases in women in 2020, making it the leading cause of cancer incidence in women globally. This is consistent with other studies, such as that by Bray et al. [9], which underscores breast cancer as a significant health concern due to its high incidence and fatality rates. The incidence of breast cancer is rapidly increasing in several Latin American and Asian countries, including Ecuador, Costa Rica, India, Japan, Thailand, and Turkey. Changes in various reproductive and hormonal factors have contributed to the rising incidence rates over the past 50 years in many higher-income countries, and more recently in lower-income ones. Following a sharp decline around 2000, incidence rates have stabilized in countries with high HDI (such as Australia, Canada, the United Kingdom, and the United States). This is likely due to the publication of influential papers highlighting the risks of menopausal hormone replacement therapy on breast cancer. The death rate from breast cancer has been consistently decreasing in many high-HDI countries, notably in Australia, Canada, and the US, where rates decreased by 18–22 % between 2002 and 2012.

This result is in line with past studies, such as those by Bray et al. [9], which emphasize breast cancer's high incidence and death rates as a serious health concern. In certain Asian nations (including India, Japan, Thailand, and Turkey) and Latin American nations (such as Costa Rica and Ecuador), the incidence rates of breast cancer are rising quickly. Changes in reproductive and hormonal factors may have contributed to the rise in incidence rates over the past 50 years in many higher-income countries, and more recently in lower-income ones. After a dramatic drop around 2000, incidence rates have stabilized in nations with high HDI (such as Australia, Canada, the United Kingdom, and the United States). This is probably due to the release of seminal papers regarding the hazards of menopausal hormone replacement therapy for breast cancer. The death rate from breast cancer has been steadily declining in several high-HDI countries, especially in the US, Australia, and Canada, where rates fell by 18–22 % between 2002 and 2012 [[10], [11], [12]].

Approximately 1.3 million new cases of prostate cancer were reported in 2018, accounting for 13.5 % of all new cancer cases in males. Prostate cancer is currently the second most common cancer in men globally. In 2018, there were 360,000 deaths from prostate cancer, representing 6.7 % of all cancer-related deaths in men. Up until the mid-1990s, prostate cancer incidence rates in the US increased gradually. This increase was partially caused by the introduction of prostate-specific antigen (PSA) testing as a method of diagnosing asymptomatic prostate cancer. There was a peak by the year 2000, followed by a decline. Incidence rates later decreased in both Australia and Canada, following similar trends. Incidence rates increased in a number of Latin American and Asian nations, including Ecuador, Costa Rica, and Turkey, before stabilizing. Prostate cancer incidence rates are much higher in Black groups, suggesting a genetic component, even if genetic variables probably do not explain much of the historical trends observed in different populations. Globally, cervical cancer ranks fourth in terms of incidence among women; in 2018, there were an estimated 311,000 fatalities and 570,000 new cases. Over the past few decades, cervical cancer incidence and mortality rates have declined in the majority of countries. In many high-HDI countries, such as Australia, Canada, the United Kingdom, and the United States, these declines appear to have stabilized, and the success of cytology-based screening programs has been credited with the declines. Nevertheless, a number of studies have discovered that younger generations of women have seen rises in a number of nations, including Finland and the Netherlands, despite overall declines in incidence and mortality rates [13,14].

In the world, cervical cancer ranks fourth in terms of incidence among women. In 2018, there were an estimated 311,000 fatalities and 570,000 new cases. Over the past few decades, cervical cancer incidence and mortality rates have declined in the majority of countries. In many high-HDI countries, such as Australia, Canada, the United Kingdom, and the United States, these declines appear to have stabilized, and the success of cytology-based screening programs has been credited with the declines. Nevertheless, a number of studies have discovered that younger generations of women have seen rises in a number of nations, including Finland and the Netherlands, despite overall declines in incidence and mortality rates [[15], [16], [17]].

2.2 Overview of cancer research and therapies

Comprehending the patterns of Cancer occurrence and mortality is crucial for developing effective public health strategies and allocating resources appropriately. Research has demonstrated that age-adjusted morbidity and death rates provide valuable insights into the impact of cancer across different regions and populations [18]. Specifically, Klimova et al. [18] investigated the physiological responses to toxins and underscored the importance of age-specific data in medical research. This principle is relevant to this study as we analyze the life expectancy data from GLOBOCAN 2020 to model cancer parameters, emphasizing the need to consider demographic variations in cancer research. This research aims to fill the gap by providing an in-depth examination of the incidence and mortality rates of cancer across genders, thereby contributing to a more nuanced understanding of global cancer trends.

Furthermore, recent advancements in nanotechnology have opened new avenues for cancer therapy. Eftekhari et al. [19] highlighted the potential of both natural and synthetic nano vectors in cancer treatment, demonstrating significant progress in targeting cancer cells more effectively while minimizing side effects. By integrating this understanding with epidemiological data, this study not only models the current trends in cancer incidence and mortality but also explores potential future applications of innovative treatment methods. The incorporation of nano vectors can enhance the predictive power of this study's models, making them more relevant for future therapeutic strategies. This connection between epidemiological trends and advanced treatment modalities underscores the importance of our research in guiding future biomedical applications.

Other recent works have further emphasized the importance of detailed demographic analysis in cancer research. For instance, Bray et al. [9] provided a comprehensive overview of global cancer statistics, reinforcing the need for detailed, region-specific studies to comprehend the differences in cancer incidence and mortality. The work by Bray et al. [9] offers critical insights into global cancer statistics, emphasizing the importance of considering regional and demographic differences in cancer research. Their study provides a foundation for understanding the global cancer landscape, which is essential for developing targeted interventions. By incorporating these insights into our model, we aim to highlight the specific regions and populations that are at higher risk, thus informing more effective public health strategies. This alignment with Bray et al.'s [9] findings ensures that this study is grounded in a comprehensive understanding of global cancer trends and contributes to the broader effort to combat cancer worldwide.

Similarly, Ferlay et al. [20] updated the global cancer burden, providing the most recent data on cancer morbidity and death. Their work underscores the increasing number of cancer cases and the need for continuous monitoring and modelling of these trends. This study builds on theirs by using the latest GLOBOCAN 2020 data to develop predictive models that can guide future research and policy decisions. By addressing the rising trends highlighted by Ferlay et al. [20], this research offers a timely and relevant analysis that supports ongoing efforts to understand and mitigate the global impact of cancer.

These studies support the necessity of this research, which aims to model these trends accurately using the latest data and provide actionable insights for healthcare policy and planning. The methodology used in this study, which leverages cumulative risk and exponential correlation techniques, is designed to capture these nuances and offer a robust framework for understanding and addressing the global cancer burden.

3 Materials and methods used

3.1 Data

Data from the GLOBOCAN 2020 database, which provides comprehensive and standardized global cancer statistics was used in this study. The data included the life expectancy of 25 states on GLOBOCAN world estimates on cancer types which were modelled. This data was selected because it covers a broad geographical range and provides detailed age-, gender-, and race-specific information necessary for robust modelling. The time period considered spans the most recent decade, ensuring that the analysis reflects current trends and provides relevant insights for contemporary biomedical applications.

3.2 Model development

The empirical model was developed using cumulative risk and exponential correlation techniques. The cumulative risk of cancer incidences was modelled effectively using life expectancy and other demographic factors. The approach used in this study is supported by prior research that emphasizes the significance of cumulative risk models in predicting cancer trends. For instance, Ferlay et al. [20] provide comprehensive data on global cancer incidences and cumulative risks, reinforcing the importance of these models in understanding cancer epidemiology.

The cumulative risk approach allowed the estimation of the proportion of the population at each time interval t at risk of developing cancer. The cumulative risk of global incidence and mortality trends for major cancer types was calculated by using the Kaleidagraph and Origin Software v.5. These tools facilitated the calculation of global incidence and mortality trends for major cancer types and enabled precise modelling of the cumulative risk. The proportion of the population N at each time interval t, that is at risk of developing cancer was estimated. The proportion of developing cancer was estimated by multiplying the proportion of the population surviving at time t interval by the annual probability of developing cancer from GLOBOCAN 2020 estimates. The probability of occurrence was then calculated from the age-, gender-, and race-specific, incidence rate per million population, taken from the Globocan 2020 Annual Report data.

3.3 Experimental conditions

To ensure accuracy and reliability, the analysis was conducted under the following experimental conditions.• Population Proportions: The proportion of the population at risk for each time interval was estimated based on the GLOBOCAN data.

• Annual Probability: The annual probability of developing cancer was derived from GLOBOCAN's age-standardized incidence rates.

• Data Homogeneity: The data's homogeneity by standardizing it across different states and demographic groups was ensured.

• Statistical Validation: The models were validated using statistical techniques to confirm the robustness and predictive power of the results.

3.4 Strategies for minimizing fitting errors

To address and minimize fitting errors in the empirical model, this research employed several robust strategies. These strategies ensured that the results were both accurate and reliable. Here is how this research addressed the issue of fitting errors.• Optimal Parameter Estimation: This research utilized techniques for estimating the optimal parameters (α and β) for the logarithmic and exponential correlation models. By fine-tuning these parameters, the model minimized the discrepancy between the observed data and the predicted values. This process involved iterative adjustments to find the best fit, which is essential for reducing fitting errors.

• Mathematical Transformations: This study used mathematical transformations to simplify the interpretation of the model and give more physical meaning to the parameters. For example, transforming Equation (1) into Equation (2) allowed for a better understanding of the relationship between the number of incidences and deaths, thereby reducing the potential for fitting errors through a more intuitive model.

• Use of Software Tools: Advanced software tools like Kaleidagraph and Origin Software v.5 were employed for cumulative risk and exponential correlation modelling. These tools have robust algorithms for curve fitting and statistical analysis, which help in identifying and minimizing fitting errors. The software's capability to handle large datasets and perform complex calculations was crucial for accurate modelling.

• Validation with Multiple Models: This research validated the findings by comparing the results from different models. For instance, the study compared linear and power-law relationships to ensure consistency. By cross-validating with multiple models, the researchers could confirm the robustness of their results and identify any fitting errors that might be present in a single-model approach.

• Statistical Analysis and Goodness-of-Fit Tests: The research included statistical analysis and goodness-of-fit tests to evaluate the accuracy of the models. These tests, such as the chi-square test, R-squared values, and residual analysis, helped in quantifying the fit of the model to the data and identifying areas where fitting errors were significant. By analyzing the residuals, the researchers could adjust the model to better capture the underlying data patterns.

• Incorporation of Empirical Data and Real-World Variables: The study incorporated empirical data from GLOBOCAN and other reliable sources, ensuring that the model was based on real-world variables and observations. This approach helped in reducing fitting errors that could arise from hypothetical or less accurate data inputs.

4 Results and discussion

4.1 Causal correlation between the number of incidences and deaths cases

The graphical representation (Fig. 1) of the logarithm of the number of incidences (lnNc) with respect to the logarithm of the number of deaths (lnNd) shows approximately a linear dependence which can be expressed as Equation (1).(1) lnNd=α∙lnNc−β

where α and β the two adjustable parameters have the following optimal parameters (α = 0.976986) and (β = 0.321396).Fig. 1 Correlation between the logarithm of the number of new (lnNd). And deaths (lnNc) cases.

Fig. 1

To give more physical meaning to the optimal parameters (α) and (β) in Equation (1), the following mathematical transformation shown in Equation (2) is suggested.(2) lnNd=α∙lnNcNc0

where Nc0 is a virtual limiting minimum value for the number of incidences (Nc) for which we have only one case of death (Nd = 1). Comparing Equation (1) with Equation (2), the value of (Nc0) is estimated as Equation (3).(3) Nc0=eβα=1.389532

The value of (Nc0) greater than the unit and the value of (α) very close to unit confirm that the number of deaths is always less than the number of incidences (Fig. 1 and Equation (1)).

However, Equations (1), (2) prove that the Nc-Nd dependence can be assumed as an approximate power low-type behaviour (Fig. 2), which is expressed as Equation (4).(4) Nd=(NcNc0)α

Fig. 2 Dependence between the number of cancer incidences (Nc) and Deaths (Nd) Cases.

Fig. 2

Equation (4) can be expressed in other forms as Equation (5).(5) Nd=k∙Ncα

where α = 0.976986 is equivalent to a behavioural index of the incidence rate, which indicates the tendency of cancer to generate deaths and is dimensionless, and k = 0.72514 is equivalent to a consistency coefficient in Table 1. Table 1 gives values of the corresponding optimal adjustable parameters. Likewise, the value of (k) is less than unit, suggesting that the number of deaths (Nd) is always less than the number of incidences (Nc).Table 1 Optimal Adjustable Parameters values of Equations (1), (4), (5)).

Table 1Correlation	α	β	Nc0	k	
Both sexes	0.976986	0.321396	1.389532	0.72514	
Males	0.974529	0.246861	1.288287	0.78125	
Females	0.979067	0.427849	1.548051	0.65191	

Another type of correlation is illustrated by Fig. 3(a) and (b), which show the power law dependence between the number of cancer incidences (Nci) and the number of cancer deaths (Ndi) of each sex separately. The high correlation (R) values in Fig. 3(a) and (b) indicate a robust positive correlation between cancer incidences and deaths among both males and females respectively, with females exhibiting a slightly higher R-square value, demonstrating an even stronger relationship. The correlation values close to 1 in both cases suggest an almost linear relationship, where increases in cancer incidences are nearly proportionally matched by increases in cancer deaths. This pattern indicates issues of late diagnosis and treatment inefficacies in both genders, as well as potential biological differences in cancer progression [21]. These insights underscore the critical need for improved cancer screening programs and effective therapeutic interventions tailored to cancer patients to enhance survival rates regardless of gender. The implications for public health strategies are substantial.Fig. 3 Correlation between the number of cancer incidences (Nc) and the number of cancer deaths (Nd). (a): for males and (b): for females.

Fig. 3

Fig. 3(a) and (b) suggest that advancements in cancer detection and treatment could potentially reduce the high correlation between cancer incidences and deaths. Additionally, the nearly linear relationship across both genders highlights the urgent need for healthcare systems to address barriers to early diagnosis and effective treatment, ultimately helping to lower cancer mortality rates [22].

The low value of k in female cases (Table 1) indicates that the increase of the number of deaths with the increase of the corresponding incidences is slower than that of the males’ situation.

Due to the causal correlations mentioned above, the study proceeded to also identify the correlations between cross-variables. For example, if there is a certain number of incidences for males, how many incidences can we have for females, and likewise for deaths? Fig. 4(a) and (b) illustrate these cross-dependencies.Fig. 4 Mutual correlation female-male between; (a): the number of cancer incidences (Nci) and; (b): the number of cancer deaths (Ndi). (●): World 2020 data; (○): calculated by Equations (6), (7); (▲): calculated by Equations (8), (9).

Fig. 4

The trend in Fig. 4(a) indicates a strong positive correlation between high cancer incidence rates in males and females, which may be due to due to shared environmental risk factors, lifestyle choices, and genetic predispositions. This highlights the need for gender-neutral cancer prevention strategies in public health policy. Fig. 4(b) also shows a strong positive correlation between cancer death rates in both genders, suggesting regions with high male cancer death rates have high female death rates. This emphasizes the impact of common factors on cancer mortality. The consistency between incidence and death correlations implies that improvements in detection, prevention, and treatment for one gender benefit the other. These findings highlight the importance of integrated public health strategies considering both gender-specific and shared risk factors to effectively reduce cancer mortality and improve survival outcomes for both males and females.

The negative curvature of the trend of data points in Fig. 4(a) and (b), inspires the suggestion of the following exponential dependence shown in Equations (6), (7).(6) Nc,f=Nc,f0(1−e−νcNc,m)

(7) Nd,f=Nd,f0(1−e−νdNd,m)

where (Ni,f0) and (νi) are two adjustable parameters.

Noting that the inverse of the parameter (νi) is very close to the corresponding value of (Ni,f0), we thought of re-optimizing the fit to have a new model with only one adjustable parameter (Ni,f0) expressed as Equations (8), (9).(8) Nc,f=Nc,f0(1−e−Nc,mNc,f0)

(9) Nd,f=Nd,f0(1−e−Nd,mNd,f0)

It is noted that Nc,f0 or Nd,f0 has a double significance. On one hand, it represents in a way the rapidity of the increase in the number of female cancers (Ni,f) with the increase of the number of cases of males (Ni,m), whether for the incidence or the death. On the other hand, it indicates a certain tendency and a kind of virtual limiting value can be reached by the number of cases of females (Ni,f) when the number of cases of males (Ni,m) takes a very high value. It is expected that this value is proportional to the global number of people of the World related to the studied year, and it can increase each year according to the world population. Table 2 presents the values of optimal adjustable parameters.Table 2 Optimal adjustable parameters values of Equations (6), (7), (8), (9).

Table 2Correlation	Nc,f0	νc	1/νc	Nd,f0	νd	1/νd	
Equations (6), (7)	6.7 × 106	1.55 × 10−7	6.45 × 106	2.75 × 106	3.5 × 10−7	2.86 × 106	
	Nc,f0	νc = 1/Nc,f0	1/νc	Nd,f0	νd = 1/Nd,f0	1/νd	
Equations (8), (9)	8.3 × 106	1.205 × 10−7	8.3 × 106	2.55 × 106	3.92 × 10−7	2.55 × 106	

4.2 Correlation of the cumulative risk with the Number of New and Death cases

Graphical representation of the Cumulative Risk (cum.risk) of cancer incidences of each sex separately against the global cum.risk of cancer incidences of both sexes (Fig. 5(a)) shows a reliable linear dependence, which can be expressed as Equations (10), (11).(10) Cum.Risk(c)m=am×Cum.Risk(c)

(11) Cum.Risk(c)f=af×Cum.Risk(c)

Fig. 5 Mutual correlation between incidences' cumulative risks. (a): the cum.risk of cancer incidences of each sex against the cum.risk of cancer incidences of both sexes; (●): for males; (○): for females. (b): the cum.risk of cancer incidences of females against the cum.risk of cancer incidences of males.

Fig. 5

Note that the values of am = 1.08769 and af = 0.927023 represent the slope of the linear regression related to the global set of data (Fig. 5(a)). The ratio (am/af), greater than the unit (1.1733), is principally due to the male-female population ratio in the world.

Nevertheless, the Fig. 5(b) exhibits a curvature change, which indicates an inflection point (F) coinciding with the South-Eastern region and indicating that for the regions beyond (F), the increase of the cum.risk of cancer incidences of females against the cum.risk of cancer incidences of males is more accentuated comparing with the regions before (F).

However, the graphical representation of the cum.risk of cancer mortality of each sex separately against the global cum.risk of cancer mortality of both sexes (Fig. 6(a)) shows a non-linear dependence, and the increase is more accentuated for males than for females. This ascertainment indicates that it is not only due to the male-female population ratio in the World.Fig. 6 Mutual Correlation between Deaths' cumulative risks. (a): the cum.risk of cancer deaths of each sex against the cum.risk of cancer deaths of both sexes; (●): for males; (○): for females. (b): the cum. risk of cancer deaths of females against the cum.risk of cancer deaths of males.

Fig. 6

Nevertheless, Fig. 6(b) exhibits a negative curvature, which indicates approximately a vertex point (V) indicating that for the regions beyond (V), the increase of the cum.risk of cancer mortality of females against the cum.risk of cancer mortality of males is more attenuated comparing with the regions before (F) and probably there is a pseudo-plateau.

Finally, different mortality-incidence correlations shown in Fig. 7(a), (b), and 7(c) exhibit similar behaviour characterized by a vertex point (V) showing an increase followed by a decrease. Fig. 7(a) shows the mutual correlation between the cumulative risk of cancer deaths in both sexes and the cumulative risk of cancer incidences in males, following a parabolic trend. Initially, higher male cancer incidences lead to a proportionate increase in deaths, but beyond a certain point, deaths do not rise as sharply, suggesting effective interventions. In Fig. 7(b), a similar parabolic relationship is seen for males, indicating that improved medical care and early detection mitigate mortality at high incidence levels. Fig. 7(c) shows a parabolic trend for females, with effective cancer management leading to lower-than-expected death rates despite high incidences, highlighting the need for robust public health policies. These findings emphasize the importance of targeted and gender-specific cancer control strategies.Fig. 7 Mutual correlation deaths-incidences in cumulative risk; (a): the cum.risk of cancer deaths of both sexes against the cum.risk of cancer incidences of males. (b): the cum.risk of cancer deaths of males against the cum.risk of cancer incidences of males. (c): the cum.risk of cancer deaths of females against the cum.risk of cancer incidences of females.

Fig. 7

4.3 Correlation male-female between the World Age-Standardized Rates

Firstly, the analysis of the mutual correlation between the World Age-Standardized Rates of females (ASRf) and the World Age-Standardized Rates of males (ASRm) can be fitted in non-linear regression for cancers for World 2020 using at least a three-degree polynomial (Fig. 8).(12) y=x(ax2+bx+c)

where a, b and c are three adjustable parameters. Notice that an x-factor has been suggested as an extrapolation method, to constrain the curve to pass through (0,0) to have a logical phenomenon for the two correlations. Assuming that all populations are mixed, then zero males automatically correspond to zero females. The polynomial form of Equation (12) exhibits an inflection point F(x0,y0) for which if a double shift is done, both in abscissa axis (x0) and in ordinate axis (y0), the form of Equation (12) can be simplified in the general form expressed as Equation (13).(13) Y=αiX(X2+βi)

where the values of optimal adjustable parameters αi, βi, (X = x – x0) and (Y = y – y0), and (x0 = ASRi,0) and (y0 = ASRj,0) are presented in Table 3, Table 4. Note that the couple of ASRi,0, ASRj,0 are the coordinates of the centre of symmetry (Fi) in the global trend of data points drawn (Fig. 8). While the values of αi-coefficients represent in a way the rapidity of the increase in the Age-standardized incidence rates of females with the increase of the Age-standardized incidence rates of males. The factor (βi) is a term ensuring the respect of the boundary conditions.Fig. 8 Correlation between the World Age-Standardized Rates of females (ASRf) and the World Age-Standardized Rates of males (ASRm). (●): Effective values for each geographical region, (○): Word average, (−): fitted values using a three-degree polynomial (Equations (12), (14)). The dashed line represents the first bisector.

Fig. 8

Table 3 Age-standardized (World 2020) incidence rates, all cancers excluding non-melanoma skin cancer, by sex.

Table 3Region	Males (x)	Females (y)	
Australia and New Zealand	328.0	294.2	
Western Europe	327.8	273.3	
Northern Europe	313.4	282.4	
Northern America	307.5	289.3	
Southern Europe	301.3	241.9	
Central and Eastern Europe	285.3	215.1	
Polynesia	248.3	215.8	
Eastern Asia	242.0	195.6	
RMS	223.10	197.28	
Mean	209.59	188.64	
Southern Africa	208.8	178.8	
World	206.9	178.1	
South America	205.6	185.1	
Median	204.59	178.8	
Caribbean	204.5	167.6	
Micronesia	202.6	165.8	
Western Asia	193.2	159.5	
Melanesia	180.8	193.0	
Inflection point (F)	173.06	159.71	
South-Eastern Asia	156.6	147.5	
Northern Africa	143.3	138.4	
Central America	134.6	136.3	
Eastern Africa	110.2	145.0	
Middle Africa	107.4	114.2	
South-Central Asia	101.8	101.5	
Western Africa	98.3	121.1	
(source: https://gco.iarc.fr/today/home)

Table 4 Optimal adjustable parameters values of Equation (12).

Table 4Correlation	αi	βi	x0	y0	
Male – Female	α1	β1	ASRmal,0	ASRfem,0	
1.3614 × 10−5	37838.24	173.06	159.71	

The empirical models can help determine the severity of cancer sickness in both men and women. Male and female incidence inflection points are 173.06 and 159.71, respectively. Male cancer rates, on average, outnumber female cancer rates. South-Eastern Asia, Northern Africa, Central America, Eastern Africa, Middle Africa, South-Central Asia, and Western Africa are not only yet to reach their inflection points in terms of cancer incidence but also have rates that are lower than the global rates of 206.9 and 178.1 for male and female cancer incidence, respectively. In terms of sex, West Africa had the lowest incidence rates. The rates in Southern America, the Caribbean, Micronesia, Western Asia, and Melanesia mostly fell between the world and inflection point rates.

The plot of the World Age-Standardized Rates of females (y = ASRfem) and the World Age-Standardized Rates of males (x = ASRmal) is shown in Fig. 8. Note that the trend of data points permits the presentation of Equation (13) as Equation (14).(14) ASRfem=α1(ASRmal−ASRmal,0)[(ASRmal−ASRmal,0)2+β1]+ASRfem,0

where α1 and β1 are two optimal parameters with positive values. The values of optimal adjustable parameters α1, β2, (x0 = ASRmal,0) and (y0 = ASRfem,0) are presented in Table 4. The values of α1-coefficients represent in a way the rapidity of the increase in the Age-standardized incidence rates of females with the increase of the Age-standardized incidence rates of males.

4.4 Correlation incidence-mortality between the World Age-Standardized Rates

Table 5 shows the age-standardized incidence and mortality rates for all cancers excluding non-melanoma skin cancer. Australia and New Zealand topped the list, with incidence and mortality rates of 309.6 and 84.7, respectively, above the expected inflection point of 229.47 and 124.31 in incidence and mortality, according to the empirical models' estimated parameters. Western Europe, Northern America, Northern Europe, Southern Europe, Central and Eastern Europe, and Polynesia all possessed this quality after these two countries in chronological order.Table 5 Age-standardized (World 2020) incidence and mortality rates, all cancers excluding non-melanoma skin cancer.

Table 5Region	Incidence (x)	Mortality (y)	
Australia and New Zealand	309.6	84.7	
Western Europe	296.9	103.0	
Northern America	296.2	86.5	
Northern Europe	295.3	99.1	
Southern Europe	267.6	98.4	
Central and Eastern Europe	239.4	118.3	
Polynesia	230.0	127.0	
Vertex point (F)	229.47	124.31	
Eastern Asia	215.8	122.8	
RMS	207.04	98.369	
Mean	196.37	96.871	
South America	192.4	90.6	
World	190.0	100.1	
Southern Africa	187.1	107.9	
Melanesia	185.3	116.9	
Median	185.30	-	
Caribbean	183.8	101.7	
Micronesia	180.6	118.1	
Western Asia	171.9	97.9	
Median	-	97.90	
South-Eastern Asia	150.0	94.7	
Northern Africa	139.9	88.9	
Central America	134.8	65.4	
Eastern Africa	127.3	90.7	
Western Africa	109.3	77.7	
Middle Africa	109.2	77.5	
South-Central Asia	101.3	66.5	
Inflection point (F)	93.82	57.915	

All other portions of the world dropped below this inflection point in terms of incidence and mortality, with Eastern Asia and southern America regions having rates that were halfway between the point of inflection and the global incidence and mortality rates. In addition to the seven locations mentioned, Eastern Asia and South America have higher incidence and mortality rates than the inflection point rates but are still higher than the global rates of 190 and 100.1, respectively. The closest regions below the world rates were Southern Africa and Melanesia. In terms of other African areas, the Northern, Eastern, and Central regions led the way in terms of incidence and death rates, followed by Western Africa and Middle Africa, however, they all fell short of the global rates. In terms of incidence and fatality rates, South-Central Asia was the least affected region.

The plot of the World Age-Standardized Rates of mortality (y = ASRmor) and the World Age-Standardized Rates of incidence (x = ASRinc) is shown in Fig. 9. Similarly, to the previous section and using the extrapolation method, there is the need to constrain the curve to pass through (0,0) to have a logical phenomenon for the correlation between the World Age-Standardized Rates for mortality (ASRmor) and the World Age-Standardized Rates for incidence (ASRinc). It is assumed that the absence of incidence automatically corresponds to zero mortality.(15) ASRmor=−α2(ASRinc−ASRinc,0)[(ASRinc−ASRinc,0)2−β2]+ASRmor,0

where α2 and β2 are two optimal parameters with positive values. The values of optimal adjustable parameters α1, β2, (x0 = ASRinc,0) and (y0 = ASRmor,0) are presented in Table 6.Fig. 9 Correlation between the World Age-Standardized Rates for mortality (ASRmor) and the World Age-Standardized Rates for incidence (ASRinc). (●): Effective values for each geographical region, (○): Word average, (−): fitted values using a three-degree polynomial (Equation (15)). The dashed line represents the first bisector.

Note that the trend of data points permits the presentation of Equation (13) as Equation (15).

Fig. 9

Table 6 Optimal adjustable parameters values of Equation (15).

Table 6Correlation	Parameters	Inflection	Vertex	
αi	βi	x0	y0	x2	y2	
Incidence – Mortality	α2	β2	ASRinc,0	ASRmor,0	ASRinc,0	ASRmor,0	
1.33054 × 10−5	55203.28	93.82	57.915	229.47	124.31	

Note that the couple of ASRmor,0, ASRinc,0 are the coordinates of the centre of symmetry (F) in the global trend of data points drawn (Fig. 9). While the values of α2-coefficient represent in a way the rapidity of the variation in the age-standardized mortality rates with the increase of the age-standardized incidence rates. The factor (β2) is a term ensuring the respect of the boundary conditions.

In addition, the trend of data points exhibits a vertex point V(x2, y2) which is typically a local maximum of curvature of where the first derivative of y = ASRmor with respect of x = ASRinc is zero (Equation (15)). Before the vertex, (ASRmor) increases with the increase of (ASRinc), while, beyond the vertex, (ASRmor) becomes decreasing with the increase of (ASRinc).

4.5 Observations

4.5.1 Causal correlation between cancer incidences and deaths

The study revealed a significant linear relationship between the logarithms of cancer incidences and deaths, described by the equation lnNd=α∙lnNc−β where the optimal parameters are α = 0.976986 and β = 0.321396. This relationship indicates that the number of deaths (Nd) is always less than the number of incidences (Nc). By transforming this equation, a virtual limiting minimum value Nco was derived, showing that for one case of death, there are approximately 1.39 incidences. This finding suggests that while incidences are high, not all lead to death, reflecting advancements in cancer treatment and early detection.

Furthermore, the power law relationship Nd=k∙Ncα with k = 0.72514 reinforces this conclusion, indicating that the increase in deaths is proportional to the increase in incidences but at a slower rate. This power law dependency, particularly the lower k value for females, suggests that females have a slower increase in mortality with increasing incidences compared to males. This gender disparity could be attributed to biological differences, access to healthcare, or variations in cancer types prevalent among the sexes.

4.5.2 Cross-variable correlations

The study also explored the correlations between the incidences and deaths of males and females. The graphical analysis illustrated in Fig. 4 showed that these cross-dependencies are significant, providing insights into how cancer trends in one gender could predict trends in the other. For example, the analysis showed that regions beyond a certain inflection point (F) have a more pronounced increase in female cancer incidences compared to males. This is particularly evident in regions like the Southeastern region where the cumulative risk of female cancer incidences against male incidences is more accentuated.

Additionally, the study identified an inflection point for cumulative cancer mortality, with males exhibiting a more pronounced increase in mortality rates. The study's findings on gender differences in cancer mortality are corroborated by extensive research. According to a systematic review by Cook et al. [23], males generally exhibit higher cancer mortality rates compared to females, a trend observed across various types of cancer. This non-linear dependence indicates that factors beyond just population ratios contribute to these trends, possibly including environmental, genetic, and socio-economic factors. This difference is attributed to factors such as biological differences, risk factor exposures, and healthcare access disparities. The identification of a pseudo-plateau for female mortality rates in certain regions suggests that while incidences may continue to rise, mortality rates might stabilize due to better healthcare interventions.

4.5.3 Age-standardized rates and global trends

The mutual correlation between World Age-Standardized Rates (ASR) for males and females was analyzed using a three-degree polynomial regression, highlighting an inflection point that differentiates regions with varying cancer dynamics. The polynomial model indicated that regions with higher male ASRs tend to have correspondingly higher female ASRs, but the rate of increase is subject to regional variances. Regional variations in cancer incidences and mortalities are well-documented in the literature. For example, Arnold et al. [12] discuss the disparities in cancer incidences across different regions, noting that areas like Southeast Asia and sub-Saharan Africa have lower incidence rates compared to Western countries. These regional differences highlight the importance of tailored cancer control strategies to address specific epidemiological profiles.

5 Conclusion

With respect to GLOBOCAN 2020 Age Standardized World Estimates, this study advances scientific knowledge regarding modelling the incidence and mortality of cancer parameters. The developed empirical models revealed a significant linear relationship between the logarithms of cancer incidences and deaths which indicates that the number of deaths is always less than the number of incidences. Also, results from the graphical analysis showed that these cross-dependencies between the incidences and deaths of males and females are significant, providing insights into how cancer trends in one gender could predict trends in the other. Furthermore, the study identified an inflection point for cumulative cancer mortality, with males exhibiting a more pronounced increase in mortality rates. This non-linear dependence indicates that factors beyond just population ratios contribute to these trends, possibly including environmental, genetic, and socio-economic factors. Again, the empirical models further revealed that regions such as South-Eastern Asia, Northern Africa, and Central America have not yet reached their global inflection points for cancer incidences, indicating potential future increases in cancer cases. On the other hand, regions like Australia, New Zealand, and Western Europe have already surpassed these points, suggesting a need for intensified cancer control measures. This study advances cancer studies as it provides a nuanced understanding of gender-specific cancer trends, crucial for developing targeted cancer prevention and treatment strategies. By elucidating the dynamics of cancer incidences and mortalities across different regions and sexes, the findings can inform public health policies and resource allocation to combat cancer more effectively on a global scale. Despite the significance of this study, it does not specifically focus on the use of green nanomaterials, the methodology employed can be adapted to evaluate the impact of such materials on cancer incidence and mortality trends. Future research could integrate green nanomaterials into the model to assess their potential as sustainable alternatives in cancer prevention and treatment.

Funding

Not applicable.

Data availability

The data used to support the study can be accessed publicly at Global Cancer Observatory: Cancer Today via https://gco.iarc.fr/today/home.

CRediT authorship contribution statement

Joseph Acquah: Writing – review & editing, Supervision, Conceptualization. Senyefia Bosson-Amedenu: Writing – original draft, Formal analysis, Data curation, Conceptualization. Francis Eyiah-Bediako: Writing – review & editing, Conceptualization. Albert Buabeng: Writing – review & editing. Noureddine Ouerfelli: Writing – review & editing, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References

1 Stewart B.W. Kleihues P. World Cancer Report, IARCPress 150 cours Albert 2003 International Agency for Research on Cancer Thomas, F-69372 Lyon, France
2 Wild C.P. Weiderpass E. Stewart B.W. World Cancer Report 2021 WHO Press, World Health Organization Switzerland 20 Avenue Appia, 1211 Geneva 27
3 Joko-Fru W.Y. Jedy-Agba E. Korir A. The evolving epidemic of breast cancer in sub-Saharan Africa: results from the African Cancer Registry Network Int. J. Cancer 147 2020 2131 2141 32306390
4 Allemani C. Matsuda T. Di Carlo V. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries Lancet 391 2018 1023 1075 29395269
5 Sung H. Ferlay J. Siegel R.L. Laversanne M. Soerjomataram I. Jemal A. Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 71 3 2021 May 209 249 10.3322/caac.21660 Epub 2021 Feb 4. PMID: 33538338 33538338
6 Wild J. El-Salahi S. Degli Esposti M. Thew G.R. Evaluating the effectiveness of a group-based resilience intervention versus psychoeducation for emergency responders in England: a randomised controlled trial PLoS One 15 11 2020 e0241704 10.1371/journal.pone.0241704
7 GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 Lancet 388 10053 2016 1659 1724 10.1016/S0140-6736(16)31679-8PMID:27733284 27733284
8 Lortet-Tieulent J. Soerjomataram I. Ferlay J. Rutherford M. Weiderpass E. Bray F. International trends in lung cancer incidence by histological subtype: adenocarcinoma stabilizing in men but still increasing in women Lung Cancer 84 1 2014 13 22 10.1016/j.lungcan.2014.01.009PMID:24524818 24524818
9 Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA A Cancer J. Clin. 70 4 2020 313 324 10.3322/caac.21660
10 Global cancer statistics GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 68 6 2018 394 424 10.3322/caac.21492PMID:30207593 30207593
11 Torre L.A. Islami F. Siegel R.L. Ward E.M. Jemal A. Global cancer in women: burden and trends Cancer Epidemiol. Biomarkers Prev. 26 4 2017 444 457 10.1158/1055-9965.EPI-16-0858 PMID:2822343 28223433
12 Arnold M. Karim-Kos H.E. Coebergh J.W. Byrnes G. Antilla A. Ferlay J. Recent trends in incidence of five common cancers in 26 European countries since 1988: analysis of the European Cancer Observatory Eur. J. Cancer 51 9 2015 1164 1187 10.1016/j.ejca.2013.09.002PMID:24120180 24120180
13 Zhou C.K. Check D.P. Lortet-Tieulent J. Laversanne M. Jemal A. Ferlay J. Prostate cancer incidence in 43 populations worldwide: an analysis of time trends overall and by age group Int. J. Cancer 138 6 2016 1388 1400 10.1002/ijc.29894PMID:26488767 26488767
14 Sierra M.S. Soerjomataram I. Forman D. Prostate cancer burden in central and South America Cancer Epidemiol 44 Suppl 1 2016 S131 S140 10.1016/j.canep.2016.06.010PMID:27678315 27678315
15 Plummer M. de Martel C. Vignat J. Ferlay J. Bray F. Franceschi S. Global burden of cancers attributable to infections in 2012: a synthetic analysis Lancet Glob Health 4 9 2016 e609 e616 10.1016/S2214-109X(16)30143-7PMID:27470177 27470177
16 Vaccarella S. Lortet-Tieulent J. Plummer M. Franceschi S. Bray F. Worldwide trends in cervical cancer incidence: impact of screening against changes in disease risk factors Eur. J. Cancer 49 15 2013 3262 3273 10.1016/j.ejca.2013.04.024PMID:23751569 23751569
17 Vaccarella S. Laversanne M. Ferlay J. Bray F. Cervical cancer in Africa, Latin America and the Caribbean and Asia: regional inequalities and changing trends Int. J. Cancer 141 10 2017 1997 2001 10.1002/ijc.30901PMID:28734013 28734013
18 Klimova E.M. Ivanov A.V. Malinovskaya E.M. Age determines the intensity of thyrotropic hormone production in response to copper sulphate intoxication Advances in Biology & Earth Sciences 3 3 2018 44 50 10.21544/abc.1243
19 Eftekhari A. Malekzadeh R. Ghahremani F. Natural and synthetic nanovectors for cancer therapy Nanotheranostics 7 3 2023 236 10.7150/ntno.55612 37064613
20 Ferlay J. Ervik M. Lam F. Colombet M. Mery L. Pineros M. Znaor A. Soerjomataram I. Bray F. Global cancer burden 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries Int. J. Cancer 148 5 2021 1358 1368 10.1002/ijc.32966
21 Mensah G. Nyarko S. Ampofo W. Elevation and its impact on malaria transmission dynamics in Mpohor district Trop. Med. Int. Health 28 2 2023 145 158 https://doi:10.1111/tmi.13740
22 Nyarko S. Ampofo W. Mensah G. Healthcare accessibility and malaria trends in central towns Health Pol. Plann. 36 9 2021 1332 1341 https://doi:10.1093/heapol/czab074
23 Cook M.B. Dawsey S.M. Freedman N.D. Inskip P.D. Wichner S.M. Quraishi S.M. …Rabkin C.S. Sex disparities in cancer incidence by period and age Cancer Epidemiol. Biomarkers Prev. 18 4 2019 1174 1182 10.1158/1055-9965.EPI-18-1105
