Chapter 2 Reducing Cancer Mortality: A Cluster Analysis of Risk Factors for Lung Cancer across EU Countries

In: Economics and Mathematical Modeling in Health-Related Research
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Dawid Majcherek
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Marzenna Anna Weresa
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Christina Ciecierski
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Abstract

This chapter compares risk factors for lung cancer and their significance for 27 countries in the European Union (EU). Drawing on data from a variety of sources, this study uses K-mean cluster analysis to investigate potentially modifiable risk factors for cancer including tobacco use, alcohol consumption, air pollution, socioeconomic status, and public expenditures on health care and their effects on lung cancer outcomes. Findings from this study show that the EU is not homogenous in terms of the effect of risk factors for lung cancer. Study results yielded four country groups, each representing different patterns in risk factors for lung cancer. The lowest rates of lung cancer mortality occur among southern European countries that includes: Italy, Spain, Portugal, Malta, and Romania. These countries present with a pattern of risk factors that include: relatively low alcohol consumption and low rates of smoking coupled with moderate population exposure to air pollutants. By contrast, another cluster of countries with the highest relative lung cancer rates includes Bulgaria, Cyprus, Greece, Croatia, Hungary and Poland. Here, rates of smoking and exposure to air pollutants are highest from among all the population groups analyzed, potentially lending a signal that these risk factors for lung cancer are most significant for this country group. Surprisingly, EU countries with the highest development levels and the highest ratio of health care spending relative to GDP, also present with a relatively high indicator of lung cancer mortality despite their relatively low rates of smoking and exposure to air pollutants. The heterogeneity among EU Member states regarding significant risk factors for lung cancer implies that cancer prevention policy needs to be tailored to individual patterns in risk factors for cancer.

1 Introduction

According to the World Health Organization (WHO), cancer ranks as a leading cause of death. In 2020, about 10.0 million cancer deaths were recorded, and 19.3 million new cancer cases were diagnosed worldwide. The global cancer burden is expected to grow rapidly reaching 28.4 million cases in 2040, a 47 percent rise when compared to the respective actual number of cases recorded for 2020. Lung cancer is one of the most commonly diagnosed types of cancer with a 11.4 percent share among the total number of new cancer cases diagnosed in 2020 (Sung et al., 2021). The highest lung cancer rates are reported in North America, Europe, and East Asia – particularly in China. In contrast, growth rates in new lung cancer cases are somewhat lower in Africa and South Asia (Mustafa et al., 2016). Nevertheless, lung cancer remains a leading cause of cancer death, resulting in nearly 1.8 million deaths worldwide or an 18 percent share of total cancer-related deaths in 2019 (Sung et al., 2021). These statistics are discouraging and call for continued research on the causes of cancer and possible ways to prevent it.

Many studies have shown that cancer might be preventable and that key risk factors for cancer are behavioral rather than related to genetic origin. These risk factors for cancer include but are not limited to: substance use and abuse, poor diet and nutrition, physical inactivity, body shape and exposure to air pollutants. Over 30–40 percent of cancer cases could be prevented through healthier lifestyles (Hofmarcher et al., 2019). Epidemiological and experimental studies further confirm that cancer incidence could be reduced through regulation of potentially controllable external factors, including environmental pollution. (Homaei Shandiz & Hadizadeh Talasaz, 2017; Turner et al., 2020). Furthermore, previous research shows that economic and social status, as measured by personal income and educational attainment, may have indirect effects on cancer incidence (Hemminki, & Li, 2003; Polak et al., 2019; Majcherek, Weresa & Ciecierski, 2020; 2021).

The motivation for this study derives from the context outlined above and aims to investigate the importance of lung-related cancer risk factors on cancer incidence in Europe while seeking out implications for cancer prevention policy for the EU. This chapter is structured as follows: Section 2 outlines recent literature regarding risk factors for cancer with the focus on lung cancer incidence. Section 3 describes the methodology used in this study. Section 4 presents the results, and Section 5 provides a discussion of the results and conclusions.

2 Lung Cancer Risk Factors – A Literature Review

Lung cancer occurs due to changes in the cells of the lungs, which grow and spread in an uncontrolled manner. This has many possible causes. Previous literature concerning risk factors for lung cancer suggests that lung cancer incidence depends on a variety of behavioral factors, such as tobacco use, poor diet and nutrition, alcohol consumption, exposure to air pollutants as well as other occupational factors (Bilello et al., 2002; Alberg & Samet, 2003; Malhotra et al., 2013; Mustafa et al, 2016). Most studies agree that cigarette use is the leading cause of both lung cancer incidence and death due to lung cancer (Alberg & Samet, 2003, Callagan et al., 2013; Kamis et al., 2021). The risk of lung cancer is estimated to be 20–40 times higher for smokers when compared to non-smokers (Ozlü & Bülbül, 2005; Walser et al., 2008; Krawczyk et al., 2021). Estimates from the International Agency for Research on Cancer (IARC) Risk Assessment suggest that smoking claims approximately 1.5 million deaths from lung cancer worldwide (Proctor, 2011). A review of epidemiological and experimental studies from eight different countries confirms this strong association between smoking behavior and lung cancer (Cornfield, 2009). The risk of developing lung cancer increases with both the duration of smoking as well as the frequency of use and the quantity of cigarettes smoked yet falls with the number of years since smoking cessation (Cornfield, 2009; Fukuda et al., 2018; Park et al., 2020). Gender differences prevail as men exhibit a higher incidence of lung cancer when compared to their female counterparts (Park et al., 2020). Although tobacco use is a major risk factor for developing lung cancer, studies also show that about one fourth of all lung cancer cases occur among patients who never smoked (Fukuda et al., 2018).1

Another health-related behavior that contributes to lung cancer incidence is alcohol consumption (Troche et al., 2015). An appreciable number of case control and cohort studies have evaluated the impact of alcohol use on lung cancer incidence. Bandera et al. (2001) provide a review of the epidemiological evidence published between 1984 and 2000 on this topic. The authors conclude that upon controlling for smoking behavior, consumption of all forms of alcoholic beverages and particularly, beer, may increase the risk of lung cancer incidence.

Risk factors for lung cancer frequently analyzed together in the epidemiological literature include: diet, sports activity and body weight. Alberg and Samet (2003) as well as Malhotra et al. (2016) reference evidence from a variety of case-control studies that confirm a protective effect of a diet rich in fruits and vegetables, and in particular, those containing carotenoids, against lung cancer. Surprisingly, obesity was also found to have some protective effect against lung cancer (Yang et al., 2012), despite obesity being recognized as a risk factor for thirteen other types of cancer (Calle et al., 2003). A meta-analysis of 31 papers on the relationship between obesity and lung cancer incidence reveals that overweight and obesity were inversely associated with lung cancer occurrence, suggesting a protective role of such risk factors against lung cancer in current and former smokers (Yang et al., 2012). In turn, a study by Patel et al. (2017) and their analysis of a cohort of US adults confirmed no association between obesity (as measured by Body Mass Index, BMI) and waist circumference with lung cancer regardless of smoking status. In this study, a similar conclusion was also formulated regarding the role of physical activity in protecting against lung cancer. Other studies find positive relationships between physical activity and reduced rates of lung cancer. When comparing lung cancer incidence among individuals reporting appreciable sports activity with respondents in the low or absent category of sports activity, Patel et al. (2019) find moderate protective effects of sports activity against lung cancer, possibly confirming, that minimizing time spent in sedentary behavior may play some role. In this study, protective associations were revealed for both lung cancer incidence and mortality in relation to physical exercise in older women, with this association being particularly stronger for women who were not obese (Wang et al., 2016). Another study by Zhong et al. (2016), covers twelve cohorts and six case-control studies involving nearly 2.5 million participants and over 26 thousand lung cancer cases also confirms protective effects of physical activity against lung cancer. McTiernan et al. (2019), in their systematic literature review on cancer prevention, quote the results from the 2018 Physical Activity Guidelines Advisory Committee of the United States Department of Health and Human Services (USDHHS) which, based on a comprehensive meta-analysis, concludes that high levels of physical activity result in a 25 percent relative reduction in lung cancer risk.

Air pollution is another important risk factor for lung cancer (e.g. Alberg & Samet, 2003; Mao et al., 216; Kamis et al., 2021; Krawczyk et al., 2021). According to the World Cancer Report from the WHO, exposure to air pollution deriving from various sources, (e.g. industrial pollution, diesel engine exhaust, households use of solid fuels) increases the likelihood of developing lung cancer. Key air pollutants include particulate matter, ambient ozone, carbon monoxide (CO), hydrocarbons, sulphur and nitrogen oxides, benzene, and certain metals (Pb, As, Cd, Ni). In 2017, air pollutants were estimated to cause over 350 thousand deaths due to lung cancer worldwide (Wild, Steward, eds., 2020, p. 116). Kamis et al., (2021) analyze key ambient emissions across the US and their association with lung cancer. Using a variety of regression models, the authors find that PM2.5, CO, sulfur dioxide, and ozone were the most hazardous over multiple timeframes. Moreover, comparative evaluation of different outdoor air pollutants and their carcinogenic hazard for lung cancer worldwide showed that particulate matter (PM) is a key agent among air pollutants causing deaths due to lung cancer (Wild, Steward, eds., 2020, p. 117). Epidemiological studies have confirmed this finding for other countries. For example, one study uses individual data from seventeen European cohorts covering over three hundred thousand members to confirm statistically significant but small association between long-term exposure to PM10 and the risk for lung cancer (Raaschou-Nielsen et al., 2013).

Hajat et al. (2021) observe that socioeconomic status (SES) may be an important modifier of the impact of air pollutants on health, including cancer, and provides a wide overview of variables used to measure SES, income, educational attainment and occupational status. This detailed research on social status and how it relates to lung cancer stresses the importance of local context in identifying patterns of lung cancer risk, which may help define potential targets of intervention regarding a more complete spectrum of risk factors for cancer (Williams et al., 2012). Similar conclusions and policy implications derive from another study, which focuses on specific profiles of lung cancer in countries or country groups broken down by development levels and measured by the Human Development Index (HDI). Inclusion of SES into this analysis allows for the identification of new opportunities to reduce the burden of lung cancer by adjusting prevention to the specific profile of country/regional risk factors for cancer (Cheng et al., 2016).

Finally, Danaei et al. (2005) offer a comprehensive study focused on a comparative assessment of nine risk factors for twelve different types of cancer across a variety of regions further categorized by income levels. Estimation of population attributable fractions for lung cancer reveal that worldwide, 70 percent of risk factors for cancer can be attributed to smoking, 11 percent to low fruit and vegetable intake, 5 percent to exposure to air pollutants with the remaining percentage ascribed to other factors (Danaei et al., 2005). The study by Danaei et al. (2005) also reveals that the role of risk factors for lung cancer differ across regions of the world economy when broken down by income levels. For example, the percentage attributed to smoking behavior as a risk factor for lung cancer was higher (86 percent) for high-income countries when compared to low and middle-income counterparts (60 percent) (Danaei et al., 2005, p. 1787). These findings suggest that the significance of individual-level risk factors for cancer is country-specific. Study results also reveal that risk factors for cancer are related to the SES of society.

Results from this literature review are in-line with the objective of this chapter, which aims to identify differences among EU countries regarding the role of risk factors in developing lung cancer. Differences as well as similarities among countries constitute a basis for clustering EU countries into similar groups, thus allowing this analysis to arrive at implications for policies seeking to curb cancer burden.

3 Methodology

Country-level cluster analysis was performed using data derived from the following sources:

  • Lung cancer mortality

    • Lung cancer deaths taken from the 2017 Global Burden of Disease Study (Institute for Health Metrics and Evaluation (IHME), 2018) (variable name: Lung mortality, data from 2015).

  • Socioeconomic status (SES):

    • GDP per capita in EUR extracted from Eurostat (Eurostat, 2020b) (variable name: GDP/capita, data from 2015).

    • Educational attainment was drawn from the UNESCO Institute for Statistics (UIS) (The UNESCO Institute for Statistics (UIS), 2020) (variable name: Years of Edu, data from 2015).

    • General domestic government health expenditure as a percentage of Gross Domestic Product (GDP) (%) derives from World Health Organization (WHO) (World Health Organization, 2020a) (variable name: healthcare (HC) spending, data from 2015).

  • Alcohol consumption:

    • Alcohol consumption in liters for the same calendar year from WHO (Division of Information, Evidence, Research and Innovation & WHO Regional Office for Europe, World Health Organization, 2020) (variable name: alcohol, data from 2014).

  • Tobacco use:

    • The percentage of the population that smokes currently derives from the European Health Interview Survey (EHIS) (Eurostat, 2020a) (variable name: smoke, data from 2014).

  • Diet and nutrition:

    • The fraction of the population that eats fruits or vegetables more than 5 times per week was also taken from the EHIS (Eurostat, 2020a) (variable name: diet, data from 2014).

  • Body mass index (BMI):

    • The fraction of the population with a Body Mass Index (BMI) equal or greater than 30 was also extracted from the EHIS data (Eurostat, 2020a) (variable name: obese, data from 2014).

    • The percentage of the population with membership in a sports club comes from Eurobarometer (European Union, 2014) (variable name: sports club membership (SC), data from 2013)

    • The ratio of the population which exercises or plays sports at least once per week. This data was taken from the Eurobarometer (European Union, 2014) (variable name: sports activity (SA), data from 2013)

  • Air pollutant measure:

    • Information on particulate matter derives from the European Environment Agency (EEA) (European Environment Agency, 2018) and includes PM10, which captures the presence of inhalable particles, with diameters that are generally 10 micrometers and smaller (variable name: PM10days – number of days when PM10 exceeds 50 µg/m3)

The cluster analysis was conducted for 27 European Union (EU) countries including: Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Germany, Denmark, Estonia, Greece, Spain, Finland, France, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, the Netherlands, Poland, Portugal, Romania, Sweden, Slovenia, and Slovakia. The analysis covers risk factors related to consumer health behaviors, air pollutants, and socioeconomic status (Hartigan & Wong, 1979). In order to limit the potentially large effect of variable variance on results, a standardization of variables was performed. The goal of this analysis is to identify groups of countries that are as similar as possible with regards to their respective risk factors for lung cancer mortality. Given earlier studies, there is a simple decision criteria available for selecting the proper number of clusters (Caruso et al., 2019; Henry et al., 2015; Ketchen & Shook, 1996). This is a multi-decision problem and additional algorithms must be developed in order to automatically resolve this issue. We empirically determined that the 4-cluster solution yielded the best match because with this split, all clusters were disjoint sets and empirical interpretation was reasonable. The cluster analysis was conducted using R (R Core Team, 2019) and the CRAN factoextra package (Kassambara & Mundt, 2020).

4 Results

Figure 2.1 and Table 2.1 provide results of the cluster analysis. The merged dataset containing information regarding lung cancer mortality, SES, various health-related behaviors and air pollutants allows for four distinguished clusters from across EU countries:

  • Cluster I (5 countries): Spain, Italy, Malta, Portugal, Romania;

  • Cluster II (6 countries): Bulgaria, Cyprus, Greece, Croatia, Hungary, Poland;

  • Cluster III (10 countries): Germany, Austria, France, Belgium, the Netherlands, Luxembourg, Denmark, Finland, Sweden, and Ireland;

  • Cluster IV (6 countries): the Czech Republic, Slovakia, Estonia, Lithuania, Latvia, and Slovenia.

Cluster I envelopes countries with the lowest rates of lung cancer deaths per 100,000 inhabitants (i.e., as shown in Table 2.A1 in the Annex, 41 deaths/100,000 in Portugal and Malta, and up to 59 deaths/100,000 in Italy) and differs most from other countries with respect to lifestyle measures. This cluster is characterized by the lowest levels of alcohol consumption across all cluster ranges, from 7.14 liters per capita in Italy to as high as 10.54 liters in Portugal. Moreover, low smoking rates prevail among countries in this cluster (i.e., from 6 percent in Portugal to 11 percent in Italy) with an overall average of only 14 percent of the adult population in Cluster I reporting smoking. However, Cluster I also captures countries with the lowest rates of fruit and vegetable intake (i.e., from 1 percent in Romania, up to 18 percent in Portugal). In addition, Cluster I is characterized by the lowest rates of sports activity (SA) (i.e., 19 percent in Malta up to 46 percent in Spain) and the lowest occurrence of sports club (SC) membership (i.e., from a low of 1 percent in Romania to a high of 7 percent in Spain and Italy). Finally, Cluster I includes countries with the low levels of reported obesity from across all cluster ranges (i.e., 9.1 percent in Romania to 10.5 percent in Italy). Although this cluster is characterized by populations with the lowest average number of years of education completed (i.e. an overall cluster average of ten years) it also envelopes those that rank second highest in terms of GDP per capita (i.e., approximately 19 500 EUR) and rates of expenditure on public health care (HC) (i.e., 5.72 percent of GDP). In addition, Cluster I is characterized by the second lowest levels of air pollutants present (PM10days) (i.e., an approximate 10 days per year when PM10 exceeds 50 µg/m3 in both Portugal and Spain).

Figure 2.1
Figure 2.1

Cluster plot for 11 indicators of lung cancer mortality across 27 countries

Source: Authors’ elaboration
Table 2.1
Table 2.1

Cluster means for 11 indications and lung cancer mortality

Source: Authors’ elaboration

Cluster II includes countries with the highest rates of lung cancer deaths per 100,000 inhabitants (i.e., from 47 deaths/100,000 in Cyprus to 93 deaths/100,000 in Hungary) and captures populations with an array of varying consumer health behaviors that distinguish this cluster from the rest. First, Cluster II includes countries with the highest possible smoking rates (i.e. 21 percent in Poland to a high of 31 percent in Bulgaria), the second highest levels of alcohol consumption (11 liters per year in Bulgaria and Hungary), the second highest in terms of adult obesity (i.e. with Hungary and Croatia reporting highest rates of obesity) and the second lowest in terms of participation in sports activity and membership in sports clubs (i.e. Bulgaria and Poland report the lowest rates of participation in both). Only 3 percent of citizens in Hungary and Poland declare eating fruits or vegetables more than 5 times weekly. SES status is low in Cluster II as it includes countries with lowest levels of GDP per capita (i.e., from 6360 EUR in Bulgaria to as high as 21 030 EUR in Cyprus), presents with the lowest fraction of public expenditure spent on health care (i.e., 4.4 percent of GDP) and captures a population with the second lowest number of years of education completed (an average of approximately 12 years). In Cluster II the air pollutant indicator is among the highest and measures approximately 43 days per year when PM10 exceeds 50 µg/m3 (i.e. ranges from 29 days in Hungary to as high as 64 days in Bulgaria).

Among countries belonging to Cluster III, lung cancer deaths per 100,000 inhabitants are second highest from across all European countries included in this analysis (i.e., from 41 deaths/100,000 in Sweden, Finland and Ireland to 70 deaths/100,000 in Denmark and the Netherlands). This cluster is dominated by characteristics representing high SES. Although the average number of completed years of education is approximately only 13, GDP per capita is high and measures 46,807 EUR per year, an average that is almost fourth times greater than reported for Clusters I and II, and three times the amount reported in Cluster IV. In addition, the ratio of public expenditures on health care is around 7.5 percent of GDP which is also highest among all clusters. Moreover, the variable PM10days, the air pollutant indicator, is lowest among clusters and amounts to only 8 days when yearly when PM10 exceeds 50 µg/m3. Health-related behaviors vary significantly within this cluster. For example, medium-ranked levels of alcohol consumption across the clusters range from 7.16 liters in Sweden to as high as 11.99 liters in Germany. Smoking rates are lowest in Sweden (one percent) and highest in France (25 percent).

While the measure for fruit and vegetable intake ranks moderately for this cluster (i.e., from 2 percent in Germany, up to 30 percent in Sweden), the number of obesity-related lung cancer deaths is lowest in Cluster III (i.e., from a low of 13 percent in the Netherlands and Sweden to a high of only 18 percent in Ireland and Finland). Cluster III also includes countries with the highest level of sports activity (i.e. from 43 percent in France to a high of 70 percent in Sweden), and the highest engagement in sports club membership (i.e., from a low of 12 percent in Finland to a high of 27 percent in the Netherlands).

Cluster IV comprises of countries with the second lowest rates of lung cancer deaths per 100,000 inhabitants among all the groups studied. Here, lung cancer death rates range from 44 deaths/100,000 in Slovakia to 55 deaths/100,000 in Slovenia and the Czech Republic. This cluster is characterized by the lowest number of completed years of education, the highest percentage of obese in the population and the highest levels of reported alcohol consumption. Although the average number of completed years of education is relatively high (i.e., from a low of 12.6 in Slovenia to 13.9 years in Estonia) this cluster also encapsulates populations with the second lowest measures of annual GDP per capita (i.e., an average of approximately 15 090 EUR), the second lowest percentage of GDP dedicated to health care spending (i.e., measures at approximately 5 percent of GDP) and the second highest ranking measure of air pollutants (PM10 days) when compared to countries belonging to other clusters. Indeed, population-based health behaviors captured by Cluster IV differ significantly from those present in other clusters. The Cluster IV countries differ from other countries due to its relatively high alcohol consumption (i.e., ranges from 14.42 liters in Lithuania to 16.64 liters in Estonia), relatively high smoking rates (range from 14 percent in Slovenia to as high as 28 percent in Latvia), highest rates of obesity when compared to other resulting clusters (i.e., 20 percent in Estonia to almost 21 percent in Latvia) combined with a medium percentage of people who eat fruits or vegetables more than 5 times per week (i.e., from 4 percent in Slovakia to 31 percent in the Czech Republic). Finally, Cluster IV includes countries with high average ranges in sports activity participation (i.e., from 36 percent engagement in the Czech Republic to as much as 51 percent engagement in Slovenia), while membership in sports clubs is low and ranges from only 6 percent in Latvia to 12 percent in Estonia and Slovenia.

In addition to cluster analysis, a multiple regression model (Table 2.A2 in the Annex) was performed in order to understand the cause and effect relationship between lung cancer mortality and risk factors for cancer. Keeping in mind the significant limitations of such models, (only 30 regions used), the regression analysis shows the directional impact of the environment, lifestyle and SES on lung cancer mortality. The results are consistent with the literature. Smoking, alcohol consumption, obesity and air pollution may lead to an increase in lung cancer mortality, while higher levels of GDP per capita and education seem to have a diminishing effect. However, only four risk factors in the regression model (i.e. air pollution, diet, sport activity and years of education) are statistically significant. Regression modelling is an ecological analysis, and interpretations of causality should be made with caution. As to correlation analysis, lung cancer mortality is positively related to smoking and air pollution (PM10) and negatively related to GDP per capita or diet.

5 Discussion and Conclusion

While risk factors for cancer are widely discussed and behavior changes have been identified as a means to protect against developing cancer, little has been written about the heterogeneity of countries regarding the importance of individual risk factors for lung cancer risk, particularly when considering economic development and education levels among individual countries. This chapter aims to fill this gap by providing new evidence about the importance of various risk factors for lung cancer mortality in EU countries. This analysis also includes country-specific contextual factors, such as socioeconomic status and level of education as well as public spending on health care as it relates to GDP. Using a k-means cluster approach, this study shows that the EU is not homogenous in terms of the impact of risk factors on lung cancer mortality (see Figure 2.2 below).

Figure 2.2
Figure 2.2

Lung cancer clustering results presented on the map of Europe

Source: Authorsʼ elaboration

Cluster I countries (see Figure 2.1) are one of four country-groups identified in this study and together are characterized by the lowest rates of lung cancer deaths per 100,000 inhabitants. This cluster encompasses southern European countries (i.e., Italy, Spain, Portugal, Malta, and Romania), which enjoy relatively high standards of living as measured by GDP per capita as well as the percentage of the country’s GDP appropriated for health care spending. Patterns in risk factors for cancer that dominate in these countries compared to other country groups can be described as relatively low smoking and drinking rates coupled with low exposure to air pollutants. Furthermore, these countries spend relatively high percentages of their GDP on healthcare. The simultaneous effects of this combination of factors may contribute to the lowest prevailing lung cancer mortality rates among the four defined EU country clusters.

At the other extreme, is Cluster II which groups together EU countries characterized by the highest lung cancer mortality rates (Bulgaria, Cyprus, Greece, Croatia, Hungary, Poland). When compared to the remaining three country clusters, cluster II countries present with low socioeconomic status and the lowest rates of expenditures on health care. The risk factors for cancer that prevail within this group include: relatively highest rates of exposure to air pollutants, the highest smoking rates, the second highest proportion of alcohol consumption, as well as a relatively low proportion of the population engaged in sports activity. Our study findings concerning Cluster I and Cluster II are in-line with the results reported by a vast number of existing epidemiological studies that stress tobacco smoking as a dominant risk factor for developing cancer (Danaei et al., 2005; Callagan et al., 2013; Kamis et al., 2021).

Surprisingly, results from this study show that the second highest lung cancer death rate occur among the most highly developed member states in the EU which enjoy the highest measures of socioeconomic status combined with relatively large public expenditures on health care. This group is captured by Cluster III and contains 10 western and northern European countries including: Germany, Austria, France, Belgium, Luxembourg, the Netherlands, Denmark, Finland, Sweden and Ireland. When compared to other clusters, the smoking rate and the air pollutant indicator are both lowest for Cluster III while sports activity and diet (i.e. weekly intake of fruits and vegetables) are highest among clusters. Finally, the alcohol intake indicator in Cluster III is relatively high in value, although within the cluster, this measure varies considerably across countries. For example, in some countries states (e.g. Sweden) alcohol is a predominant risk factor for cancer, while in others (e.g. France) tobacco smoking appears to be more significant.

The remaining cluster of EU states form Cluster IV and include the Czech Republic, Slovakia, Estonia, Lithuania, Latvia, and Slovenia. Countries captured by this cluster present with the second lowest lung cancer mortality from among all four identified country groups. Cluster IV is characterized by low levels of GDP per capita and low ratios of expenditures on health care. While the measure for air pollutants is low, rates of alcohol consumption is highest when compared to other clusters. Smoking rates are also relatively high. These behavioral and environmental risk factors are likely compensated for by the moderate obesity rates, intensive sports activity (i.e. the second highest among country groups analyzed in this study) and moderate fruit and vegetable intake. Favorable health behavior outcomes among these countries play a protective role against lung cancer (Alberg and Samet, 2003; Malhotra et al., 2016), and particularly among current and former smokers (Yang et al., 2012). Nevertheless, the patterns in risk factors for lung cancer among these two EU country clusters (Cluster III and IV) cannot be easily explained by referencing results of epidemiological studies. Smoking was proved to be the most important risk factor for lung cancer incidence (Alberg, Samet, 2003, Danaei et al., 2005; Kamis et al., 2021). Moreover, the risk for cancer continues to grow as smoking duration, quantity smoked and frequency of smoking rises. By the same token, risk decreases when the number of years since smoking cessation increases (Cornfield, 2009; Fukuda et al., 2018; Park et al., 2020). Passive smoking is an important yet missing consideration for non-smokers. Indeed, the need for a more detailed analysis exists, and begs for such considerations to be included in the analysis before settling on a comprehensive risk assessment for these two groups of countries.

Extensions of this study could also entail inclusion of other environmental factors (e.g. asbestos, radiation) as well as measures of the prevalence of screening programs, and varying methods of lung cancer treatment. Nonetheless, as this study shows, socioeconomic status of the population and expenditures on health care may constitute critical modifiers of lung cancer mortality. Therefore, continuing to include SES in further research on risk factors for cancer may shed new and important light on collective impact.

This chapter highlights some of the differences regarding the importance of individual risk factors for lung cancer in EU countries. Chapter findings imply that it may be more beneficial to tailor cancer prevention policy to the behavioral and environmental patterns associated with each of the EU country clusters revealed in this chapter. From a public health perspective, diverse policy measures should be taken to more effectively decrease lung cancer incidence and mortality across the EU. A holistic, problem-oriented, target approach to public health policy, including policy aimed at curbing lung cancer burden specifically, should be considered. Targeting risk factors and designing public policy actions that influence lung cancer incidence in a directed fashion (i.e., tobacco and alcohol restrictions, tobacco and alcohol price policy through taxation, education about benefits and risks for personal health) might be most effective, particularly when policy is systemic, broad-based and tailored to specific patterns in risk factors for lung cancer as they pertain to groups of similar country populations and environments.

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Annex

Table 2.A1
Table 2.A1
Table 2.A1

Cluster analysis data by country

Source: Authors’ elaboration
Figure 2.A
Figure 2.A

Cluster dendrogram based on hierarchical clustering

Source: Authors’ elaboration
Table 2.A2
Table 2.A2

Multiple linear regression results for normalized lung cancer mortality

Source: Authors’ elaboration
1

Fukuda et al. (2018) explain that increases in cancer incidence among non-smokers is due to two mutations: one, in the epidermal growth factor receptor gene (EGFR) while the other is a chromosomal rearrangement involving the anaplastic lymphoma kinase gene (ALK). Unfortunately, regardless of smoking status, the etiologies of these in regards to lung cancer remain unknown.

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