The death rate deriving from Cancer is a concern of great importance to public health as various environmental management factors are seen to exert influence on levels of occurrence (1). Ecological management focuses on sustainability by addressing environmental issues through strategic decision-making and actionable policies. It involves mitigating risks, allocating funds for ongoing improvements, and addressing past ecological damage (2).

This study categorizes environmental factors influencing public health into three main sub-factors. The first is attributed to national public authorities and the lack of strength of their investment strategies to have a relevant influence on GDP in United States Dollars (USD - $), sequentially exposing citizens to a negative consequence. The second sub-factor is the regional environmental occurrences, such as the pollution of the air and soil contamination from chemicals, in addition to the availability of adequate clean water. The final sub-factor is regarding the chosen behavior of individual citizens, namely the use of tobacco products and obesity levels. Furthermore, alternative forced determinants such as unstable physical and psychosocial impositions with origins in both personal and work-life tensions (3).

Advances over time have enhanced understanding of how environmental factors influence the risk of cancer, thus underscoring their global domination on Cancer prevalence (4). Public health continues to endure the challenges presented by Cancer, which exists as an initiator of premature death worldwide, exacerbated even more by the pattern of increasingly unhealthy lifestyle habits. Studies suggest that 40% of cancer cases in affluent countries are preventable through lifestyle changes, including increased physical activity, healthier diets, and reduced smoking and alcohol use (5). Modern advancement in the ability to enhance the speed of detection and thereafter rapid clinical treatments with supportive care has led to reducing the mortality rate of Cancer patients. Since 2019, it has been noted that the relative 5-year survival rate for a combination of Cancers has reached a figure of around 68%, prompting the rise of survival to more than 16.9 million Cancer sufferers in the United States (USA). Regrettably, many of these survivors are unable to return to normal life, often enduring long-term health problems along with a lower quality of life than previously experienced. Cost-effective interventions are key to confronting the ongoing burden of Cancer as well as battling against the rate of mortality caused by the illness. The crucial risk factors of Cancer, which are modifiable, enabling higher survival prospects, include the absence of a healthy diet, a higher than advisable body mass, inadequate physical exercise, and use of tobacco products. Past epidemiological studies have typically presumed that lifestyle behavior at one point in adulthood is a lifetime habitual behavior, accepting lifestyles are constant. Prevention strategies to fight Cancer aim to encourage behaviors that are sustainable and flexible enough to withstand behavior variability throughout life. The total significance of life changes during adulthood has still not been fully understood (6). The largest impact on public health is a habitual unhealthy lifestyle, which includes excess eating and drinking of alcohol, lack of exercise, poor quality diet, and smoking. Over 50% of the total diagnosed Cancer cases and over 40% of cardiovascular illnesses, including heart disease, are considered to be in some form associated with factors relating to detrimental lifestyles (7).

Between the years 2005 and 2015, cancer occurrence has significantly risen by 33%. This has been mainly attributed to the growth and aging of populations. Despite this rise, it is estimated that modification of lifestyles could save more than a third of the lives lost due to Cancer. A key feature of global Cancer control is primary prevention, adjustment of certain factors could change outcomes, including smoking (21%), lack of physical exercise (2%), the use of alcohol (5%), obesity (2%) and the consumption of fresh fruit and vegetables (5%) (8).

The purpose of this research is to evaluate the association between Cancer death rates and various environmental and lifestyle factors, including GDP in USD, pollution, population size, smoking and obesity in the countries of the G7, namely Germany, the United Kingdom (UK), the USA, Canada, France, Italy and Japan. The study will explore how the Cancer death rates are affected by the increasing or decreasing trends in GDP in USD, obesity rates, the effects of pollution, population size, smoking rates, and life lost in the particular countries.

This study used an ecological study design to assess selected environmental and lifestyle factors on the Cancer mortality rates. Lifestyle habits, such as obesity rates and tobacco use and environmental factors, including pollution, population size, and life expectancy were studied in relation to Cancer mortality rates in G7 countries, including Germany, the UK, the USA, Canada, France, Italy and Japan. The definitions of the data used are provided in the Table 1. The data for the study were collected from the website of Organization for Economic Co-operation and Development (OECD) and World Bank. The study focused on G7 countries, since they are the countries that have more consistent advancement in the world and long-term data for the period of 2000–2020 were well-established due to their comprehensive health data systems. Nations not included in the G7 group were excluded from the analysis, because of inconsistency in their levels of social and economic evolution, reflecting to the poor quality of the data available for them.

Significant findings relating to the dominance of the components attributed to lifestyle and environmental factors along with their significance on cancer patient mortality rates within the G7 countries over 20 years (2000 to 2020) were produced from the data analysis of this study.

147 total data points are presented in this study without any data absence. Consequently, the data absence denotes the nonexistence of a response for observation purposes. However, confusion with a zero value should be avoided. Confirmation of the completion of data concerning death deriving from cancer, the GDP in USD, the effects of pollution, the level of tobacco product use, and the loss of life from the statistical test analysis. Consequently, data relating to the research displayed a non-existence of missing values in the results obtained.

The descriptive statistics produced an evaluation of the crucial set of values involved in the study. Within the countries of the G7 during the period of research, the mean rate of cancer-caused deaths was in the region of 224.915, with a standard deviation of 21.7615 (Table 2). The means of death caused by Obesity levels and the effects of pollution saw specifically elevated means, which demonstrated the extensiveness experienced in the prosperous countries. Exhibiting a marked variance in the sizes of a population, the prevalence of using tobacco products, in addition to the conjecture of length of life, illustrated the demographic diversity of the G7 countries. (Note: as the correlation of death caused by cancer was negligible, GDP in USD was not used.)

In Table 3, the R-squared and Durbin-Watson statistics are important. The R-squared is 0.580, meaning that the research model’s independent variables explain 58% of the variance in the dependent variable. The remaining 42% is due to other factors. The Durbin-Watson statistic is a test for autocorrelation in the residuals of a regression model. The DW statistic ranges from zero to four, with a value of 2.0 indicating no autocorrelation. Values below 2.0 indicate positive autocorrelation. The statistical results show positive autocorrelation.

In this instance analysis of factors deriving from the environment and a dependent variable was performed using Multiple linear regression, it can examine the linear relationship betwixt a minimum of two independent variables (15). In a similar context, the linear relationship between the dependent variable (the cancer-causing death rate) and several independent variables was examined (GDP in USD, the existing level of obesity, effects originating from pollution, the population size, and the prevalence of smokers). Data analysis, estimation, and understanding of causal relationships were the purposes of this method, to determine the influences of independent variables on the dependent variable. The determination of the effect of variables such as smoking tobacco products and obesity on the cancer mortality rate is established by multiple linear regression. Table 4, namely the coefficient table, exhibits the influence of the independent variables on the dependent variable, cancer death. The unstandardized coefficients which are as follows:

How each independent variable affects the rate of death from cancer is illustrated in this equation. For example, a decrease in the death of cancer by 0.206 units is seen when an increase in the obesity variable occurs, whilst a rise in the effects of pollution denotes an increase in death deriving from cancer by 0.048 units. Since the measurement of multicollinearity in a regression analysis is key for model fit, the Variance Inflation Factor (VIF) value is of crucial importance. The Multi-collinearity occurs upon an intercorrelation of two or more independent variables found in a multiple regression model, considering the possibility of negative impact of this intercorrelation on the regression results. An indication that multi-collinearity exists is generally exhibited by either a VIF above 4 or a tolerance below 0.25; accordingly, additional research is necessitated. Hence, a similar situation for the independent variables was not detected.

Figure 1, a correlation matrix was created showing the relationship between all variables through a color palette. The values in the correlation matrix range from −1 to +1. Values close to −1 indicate a negative correlation, while values close to +1 indicate a positive correlation. Two variables with a positive correlation will increase or decrease together, whereas for two negatively correlated variables, as one increases, the other decreases. A value close to 0 indicates no connection between the two variables.

In consideration of the data given in the table, between 2000 and 2020, there was a positive correlation between cancer deaths and smoking, life loss, pollution effect, obesity, and GDP rates. As these rates increase, the cancer death rate also increases in parallel. The highest positive relationship is with the smoking rate, at 0.44. Therefore, the smoking rate affects the cancer death rate more than the other variables. On the other hand, there is a negative correlation between cancer deaths and population. Thus, as one value increases, the other decreases. For example, as cancer deaths increase, the population decreases, or as the population rises, cancer deaths decrease. The GDP USD per hour worked value is close to 0 (−0.08), indicating no connection between these variables.

The Principal Component Analysis (PCA) reports the factors that are more dominant and illustrative in each country. PCA is a statistical technique universally used in numerous fields, including data visualization, dimensional reduction, data compression, and data analysis. It is specifically convenient for reducing complexity in large and intricate data sets, producing results that make interpretation elementary. This analysis is vital when working with complex and extensive data (16).

Figure 2 shows the contribution rates of variables to the first (Dim-1) and second (Dim-2) principal components (PCs). About Dim-1 (41.1%), smokers and population variables make the highest contribution, while pollution, GDP, and cancer deaths make moderate contributions. The obesity variable has a relatively lower contribution. This shows that the first component is dominated by smokers and the population. On the other hand, with Dim-2 (22.6%), Obesity and Death Cancer make the highest contribution. Smokers, Population, and GDP have a low effect. Pollution makes the lowest contribution to the second component.

Figure 2 shows that Dim-2 is also closely related to obesity and cancer deaths, but other variables also have a certain effect. In general, the first component (Dim-1) is most associated with smokers and population variables, while the second component is associated with obesity and cancer deaths. This graph shows the directions of the variables and the distribution of the countries. Cancer death and obesity variables appear to be strongly associated with each other (positioned close to the same direction). GDP and Population are close to each other but oriented in a different direction. GDP and pollution appear to be very closely related to each other. Smokers, on the other hand, are looking in a different direction, meaning that they may be more independent of the other variables. Considering the distribution of countries, for instance, the United States (USA) is most likely located in the direction of obesity and population (with the possible high obesity and cancer death rates). Japan (JAP) may be different from the other groups since it probably has low obesity and low cancer death rates. European countries (DEU, FRA, ITA, GRB) may generally be closer to GDP and Population. This biplot analysis is quite significant in terms of visualizing the relationship between countries’ obesity, cancer deaths, smoking rates, and economic indicators. In particular, the strong relationship between obesity and cancer deaths and the fact that the GDP and population are on a different axis are striking.

This study emphasized the intricate interrelationship between cancer mortality and various environmental, socioeconomic, and lifestyle factors in the group of countries known as the G7 countries. Smoking, obesity, pollution, and the opportunity of healthcare emerged as critical determinants. The outcome highlights the need for comprehensive public health strategies that address these key factors through preventive interventions. By exploiting the available substantial resources in developed nations, policymakers must implement effective smoking cessation programs, promote healthier lifestyles, and adopt stringent environmental regulations. Improving healthcare infrastructure and addressing socioeconomic disparities are also essential to reduce cancer mortality and enhance public health outcomes. Future research should explore the relationships of cancer mortality with various risk factors in populations and focus on developing targeted interventions tailored to these risk factors to achieve sustainable reductions in cancer mortality globally.