Global socioeconomic inequalities in breast, cervical, ovarian, and uterine cancers incidence, mortality, disability-adjusted life year’s rates: a relative concentration index analysis

Abstract

Backgrounds

This study examined the impact of the Human Development Index (HDI) on the incidence, mortality, and disability-adjusted life-year (DALY) rates of breast and gynecologic cancers in women worldwide.

Methods

We obtained the 2021 cancer rates by country from the Global Burden of Disease website. Using relative concentration indices and concentration curves, we measured socioeconomic inequality in the mortality, incidence, and DALY rates of these four cancers. We also grouped countries based on their socioeconomic status as measured by the Human Development Index.

Results

The incidence, mortality, and DALY rates of uterine and ovarian cancer show higher values in countries with higher levels of HDI (all concentration indices were significantly higher than zero). On the other hand, the incidence, mortality and DALY rates of cervical cancer were more concentrated in countries with lower levels of HDI (all concentration indices were significantly less than zero). No socioeconomic inequality was observed in breast cancer mortality (RCI = 0, 95% confidence interval (CI) = -0.03 to 0.04), there was no significant inequality in breast cancer DALY rates (RCI = 0.02, 95% CI: -0.02 to 0.06), and only breast cancer incidence was significantly concentrated in countries with a high HDI (RCI = 0.09, 95% CI: 0.06 to 0.13).

Conclusion

Our study showed that socioeconomic inequalities exist in the distribution of mortality and morbidity from breast and gynecological cancers. Health care policies and facilities to reduce socioeconomic inequalities should focus on areas of high burden.

Introduction

Breast (BC) and Gynecologic Cancers are among the most prevalent diseases affecting women. Notable among these diseases are BC, Uterine Cancer (UC), Cervical Cancer (CC), and Ovarian Cancer (OC), all of which impact the female reproductive system [1]. Each type of cancer has distinct risk factors, treatment approaches, and outcomes. BC, are among the leading causes of death in women worldwide [2].

Inequality refers to the unequal distribution of resources among individuals within a society. The term can have different meanings to different people and in various contexts. Inequality encompasses distinct economic, social, and spatial dimensions [3]. Economic inequality pertains to disparities in income and wealth, while social inequality involves unequal rights and access to opportunities and social welfare or public goods based on factors such as race, ethnicity, age, gender, sexuality, disability, citizenship status, or residential status [4]. Spatial inequality highlights the geographic aspects of inequality, such as disparities between regions and urban versus rural areas. Importantly, these forms of inequality can be interconnected in complex ways across time and space [5].

Inequality in the prevalence and incidence of female BC and genital cancers is a significant health and social issue that has garnered attention from researchers and health policymakers [6]. These inequalities are influenced not only by biological and genetic factors but also by social, economic, and cultural factors [7]. Research indicates that women from different social and economic backgrounds face varying risks of developing these cancers due to several reasons, including unequal access to healthcare services, insufficient education on prevention and early detection, and cultural differences in attitudes towards health and illness [8].

While the etiology and treatment of various types of BC and gynecologic cancers have been extensively studied, many aspects of the disease remain unknown, and socio-economic factors have been studied less frequently. This study aimed to assess the relationship between specific components of the Human Development Index (HDI) (namely, life expectancy at birth, education, and gross national income per 1,000 capita) and the rates of incidence, mortality and disability adjusted life years (DALY) rates associated with BC and gynecologic cancers. Additionally, it sought to examine the socio-economic inequalities in global incidence, mortality and DALY rates of BC and gynecologic cancers.

Methods

Data sources and definition

Data on the incidence, mortality, and disability-adjusted life years (DALY) rates associated with BC (ICD-10-CM Diagnosis Code C50.919), OC (ICD-10-CM Diagnosis Code C56.9), CC (ICD-10-CM Diagnosis Code C53.9) and UCs (ICD-10-CM Diagnosis Code C55) in women worldwide were extracted from the Global Burden of Disease website. This website is publicly available at https://ghdx.healthdata.org/gbd-2021. In brief, the Global Burden of Disease provide a thorough estimate of the impact of 369 diseases and injuries and 87 risk factors, all categorized by sex, age, region and country. In addition, data on the HDI 2021 and its components were extracted from the World Bank database for 189 countries (http://www.worldbank.org/). In brief, the HDI is a composite measure of a country's overall social and economic development. It focuses on three main dimensions: health, measured by life expectancy at birth; education, represented by both mean years of schooling and expected years of schooling; and standard of living, indicated by gross national income per capita. The HDI is used to rank nations and assess their development progress, emphasizing human well-being in addition to economic performance. The value of the HDI varies between zero and one, with values closer to one indicating a higher level of development and values closer to zero indicating a lower level of development. In this research, the HDI values of different countries were used to classify their socio-economic status.

Data validation

Validating cancer data in a Global Burden of Disease study is a complex process that requires meticulous attention to various methodological steps to guarantee that the resulting information is accurate, dependable, and thorough. This validation process starts with a comprehensive assessment of data sources, which can include cancer registries, medical records, health surveys, and vital statistics. It's crucial to evaluate the quality, credibility, and geographic and temporal coverage of these sources to ensure they represent diverse populations. The International Classification of Diseases definitions are then employed to establish consistent definitions and classifications for cancer types, stages, and outcomes. Following this, various statistical methods, such as meta-analysis or Bayesian models, are applied to combine data from multiple sources, addressing any variability and biases present. In meta-analytic approaches, researchers systematically review and combine the results of multiple studies on specific health outcomes. This enables them to calculate pooled estimates of incidence, prevalence, or mortality rates. Thus, statistical power is enhanced, providing more robust estimates. Additionally, Bayesian statistical models are applied to GBD data to account for uncertainty and incorporate prior information. Bayesian hierarchical models estimate health outcomes while considering factors such as age, sex, geography, and time. Bayesian methods can also generate or impute missing data within the GBD framework. For instance, if certain regions lack comprehensive data on a specific health outcome, Bayesian models can estimate the missing values using observed data from comparable populations or prior distributions. Age standardization techniques are also utilized to ensure that populations and demographic groups can be compared meaningfully. The GBD dataset uses age-standardization techniques to adjust for differences in age distribution among populations. This allows for valid comparisons of health outcomes and disease prevalence among different demographic groups. Using a standard age distribution — typically the World Health Organization's World Standard Population — allows GBD to control for age-related variations in health data. This provides a clearer picture of the disease burden independent of age structure. This approach makes findings more relevant and interpretable across different regions and populations, which facilitates effective public health interventions and resource allocation. Subsequently, to validate the data and assess the accuracy of the estimates, the results from the models undergo rigorous validation and consistency checks against other data sources, such as World Health Organization (WHO) data, national cancer registry information, and peer-reviewed literature. This process includes cross-referencing reported statistics and conducting sensitivity analyses. Generally, Cross-checking GBD data sources involves systematically comparing data from several reliable sources to ensure accuracy and consistency. This process includes verifying statistical information, data collection methods, and reporting standards to identify discrepancies and anomalies. Expert consultations and peer reviews are also used to validate findings and improve the credibility of the data.

Data analysis

The Relative Concentration Index (RCI) was employed to assess socioeconomic disparities in mortality, incidence, and DALY rates for BC, OC, CC, and UCs. The RCI is a relative measure calculated from a concentration curve, which evaluates socioeconomic inequality across ordinal variables and groups. In the concentration curve, the rates of mortality, incidence, and DALYs for BC, OC, CC, and UCs (on the y-axis) were plotted against the cumulative population proportion ranked by HDI (on the x-axis). The curve begins with the lowest HDI values on the left and progresses to the highest HDI values on the right. This relative measure ranges from + 1, indicating that the cancers are entirely concentrated in high HDI countries, to −1, meaning they are entirely concentrated in low HDI countries. A value of zero signifies no inequality. When the RCI is zero, it indicates that there is no socioeconomic disparity in the incidence, mortality, and DALYs of BC, OC, CC, and UCs among countries with varying HDI levels. If the concentration curve rises above the diagonal line (45-degree line), the RCI is negative, suggesting that the outcomes are concentrated in low HDI countries. Conversely, if the concentration curve falls below the diagonal, the RCI is positive, indicating that the outcomes are disproportionately concentrated in high HDI countries. The data were analyzed using Stata software version 17.

Results

Socioeconomic inequalities in BC mortality, incidence, and DALY rates at the global level in 2021

The results of the concentration curve depicting socioeconomic inequality in incidence, mortality, and DALYs from women's cancers (BC, OC, CC, and UC) are shown in Fig. 1(A-L). In this figure, the horizontal axis represents the cumulative percentage of countries ranked by HDI. A score of 0 indicates the lowest HDI score, and a score of 1 indicates the highest HDI score. The vertical axis shows the percentage of BC, OC, CC, and UC incidence, mortality, and DALYs corresponding to the cumulative percentage of HDI. The RCI and 95% CI were zero (−0.03 to 0.04) for the BC mortality rate, 0.09 (0.06 to 0.13) for the BC incidence rate, and 0.02 (−0.02 to 0.06) for the BC DALY rate. The zero RCI value for the mortality rate indicates the absence of inequality, and the positive value of the RCI and the position of the concentration curve under the equity line indicate that BC incidence rates are concentrated in countries with a high HDI ranking. The positive RCI value for the DALY rate attributed to BC indicates its greater concentration in high HDI countries, although this difference between high and low HDI countries is not significant (Table 1, Fig. 1A-C).

Fig. 1.

Concentration curve for measuring socioeconomic inequality in the mortality, incidence and DALY of breast, ovarian, cervical, and uterine cancers by Human Development Index (HDI) cumulative percentage of the countries worldwide

Table 1.

Relative concentration index (RCI) in measuring socioeconomic inequality in mortality, incidence and DALY rates of common women cancers in 2021

Outcome	RCI (95% CI)
Breast cancer mortality	0.00 (−0.03 to 0.04)
Breast cancer incidence	0.09 (0.06 to 0.13)
Breast cancer DALY	0.02 (−0.02 to 0.06)
Ovarian cancer mortality	0.04 (0.01 to 0.08)
Ovarian cancer incidence	0.08 (0.04 to 0.11)
Ovarian cancer DALY	0.04 (0.01 to 0.06)
Cervical cancer mortality	−0.18 (−0.26 to −0.11)
Cervical cancer incidence	−0.11 (−0.17 to −0.05)
Cervical cancer DALY	−0.18 (−0.25 to −0.10)
Uterine cancer mortality	0.18 (0.12 to 0.24)
Uterine cancer incidence	0.23 (0.16 to 0.30)
Uterine cancer DALY	0.18 (0.12 to 0.24)

Socioeconomic inequalities in OC mortality, incidence, and DALY rates at the global level in 2021

The RCI were 0.04, 0.08, and 0.04 for the OC mortality, incidence, and DALY rates, respectively. Positive and significant values of the RCI, as well as the placement of the concentration curve below the equality line, indicate a greater concentration of OC deaths, incidence, and disabilities in countries with a high HDI (Table 1, Fig. 1D-F).

Socioeconomic inequalities in CC mortality, incidence, and DALY rates at the global level in 2021

Negative value of concentration index in Table 1 and also results from Fig. 1G-I for mortality (−0.18), incidence (−0.11), and DALY (−0.18) rates of CC indicated that the rates of this cancer is more concentrated in low HDI countries. According to Fig. 1G, 20% of the world's population with the lowest HDI rank accounted for approximately 38% of CC deaths worldwide. On the other hand, 20% of the world's population with the highest HDI accounted for 20% of CC deaths worldwide. Alternatively, the poorest 30% of the world's population accounted for half of the global CC deaths. In terms of CC cases, the 20% of the world's population with the lowest and highest HDI rank accounted for approximately 35% and 20% of the world's CC cases, respectively (Fig. 1H).

Socioeconomic inequalities in UC mortality, incidence, and DALY rates at the global level in 2021

The positive concentration index values in Table 1, along with the data from Fig. 1J-L, show that UC rates—mortality (0.18), incidence (0.23), and DALY (0.18)—are more prevalent in countries with a high HDI. Figure 1J indicates that the bottom 20% of the global population, ranked by HDI, accounted for approximately 8% of all UC deaths worldwide. Additionally, the poorest 40% of the world's population was responsible for 18% of global UC fatalities. Regarding UC incidence, the lowest 20%, 30%, and 40% of the world's population accounted for around 5%, 8%, and 10% of global cases, respectively, as shown in Fig. 1K. For the DALY index, the poorest 20% of the world's population accounted for 10% of global UC DALY, according to Fig. 1L.

Discussion

Investigating inequalities in cancer incidence, mortality and DALY rates among women worldwide is extremely important. This issue is not only relevant to women's health and quality of life but also impacts the social and economic development of societies [7]. Various factors, including biological, social, and cultural influences, may put women at higher risk for developing certain types of cancer [7]. Inequities in access to healthcare, education, and cancer awareness—particularly in low- and middle-income countries can result in late diagnoses and poor treatment. This impacts women's health and burdens families and healthcare systems [9]. Therefore, gaining a comprehensive understanding of these inequalities is crucial for designing effective policies and programs that promote women's health and reduce the incidence of cancer.

BC is the most common cancer diagnosed in 2020, with 2 million new cases. The incidence and mortality rates of this disease have increased over the past 3 decades [10]. The availability of mammography as a reliable screening tool [11], along with policies aimed at reducing the burden of BC in many countries [12] and increased awareness among women about the disease [13], has contributed to a rise in the incidence of this malignant tumor globally. However, the high rate of BC in countries with a high HDI can be attributed to several factors. These include a greater prevalence of risk factors associated with BC (early age at menopause, advanced age at first birth, limited breastfeeding, use of menopausal hormone replacement therapy and oral contraceptives, higher body mass index, lack of physical activity) in these societies, as well as the availability of well-established screening programs [14, 15].

The results of this study indicated that there is no inequality in BC mortality rates among different countries. One possible reason for this finding could be deficiencies in data collection in countries with low HDI, which may lead to the undercounting and underreporting of BC death rates [16]. Also, international initiatives aimed at improving BC care in countries with low HDI may have resulted in better outcomes for patients. Programs funded by global health organizations can enhance the detection, treatment, and education surrounding BC [17]. In conclusion, although one might expect to see disparities in BC mortality rates based on HDI, these international initiatives and support have helped create a more equitable situation. In addition, in high HDI countries, disparities in healthcare access due to socioeconomic status, race, and ethnicity contribute to higher mortality rates among low-income and marginalized groups, despite effective treatments. In low HDI countries, economic constraints further limit access to treatments, which results in higher BC mortality rates among impoverished communities in wealthy and poorer countries, highlighting a common issue.

DALYs represent the total years of healthy life lost due to illness and disability, and the burden of cancer mortality in terms of years of life lost [18]. Generally, individuals in countries with a higher HDI tend to live more years with disability, while those in countries with a lower HDI experience a greater burden of premature mortality. As a result, although countries with higher HDI have a greater share of the BC incidence burden, the DALY for BC is relatively similar in both high and low HDI countries [19].

OC ranks as the eighth most common cancer globally, accounting for 3.7% of all cancer cases and 4.7% of cancer-related deaths among women in 2020. In developed countries, it is the sixth most prevalent cancer and the fifth leading cause of cancer death among women [20, 21]. Before the year 2000, the age-standardized incidence of OC was highest in North America and Northern Europe; however, these rates have since declined in these regions while increasing in Asia and Eastern Europe [21].

UC is the fourth most common cancer among females and the sixth leading cause of cancer-related deaths in the U.S. In 2023, an estimated 66,200 new cases and 13,030 deaths are expected. Its incidence is rising and is projected to surpass colorectal cancer by 2040, becoming the third leading cancer among females and the fourth leading cause of cancer death [22]. In 2021, the age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDR) of UC in China were 6.65, 46.52, 1.24, and 37.86 per 100,000 population, respectively [23]. In the UK, UC is the fourth most common cancer among females, with approximately 9,800 new cases diagnosed each year. This type of cancer accounts for 5% of all new cancer cases in females in this country, the incidence rates of UC are highest in women aged 75 to 79 [24].

UC has three main histologic types: endometrioid, non-endometrioid, and sarcoma. Endometrioid cancers, comprising about 75% of cases, have the best prognosis. Non-endometrioid cancers, including serous and clear cell carcinomas, account for 15–20% and are more aggressive with poorer outcomes. Uterine sarcomas, arising in the myometrium, are the rarest and least studied [22]. The results of this ecological study indicated that increasing HDI and its components caused the incidence, mortality, and DALY rates of OC and UC to increase.

Early detection and screening for OC and UC are challenging because the disease often lacks specific symptoms. While there is a global shortage of reliable screening methods, countries with high HDI have an advantage due to better healthcare systems and greater access to modern imaging techniques. Consequently, the incidence rate of OC and UC are higher in these countries [25]. In contrast, in low HDI countries, it is not uncommon to fail to diagnose OC and UC until it has metastasized or misdiagnose it for a different type of cancer or even a different disease [25]. In high HDI countries, some risk factors such as overweight, obesity, smoking, and nulliparity occur more frequently than in low HDI countries, leading to a higher incidence of OC and UC [26, 27].

One of the causes of inequality in OC and UC mortality rates could be late diagnosis and lack of access to adequate treatment, including complex surgeries and chemotherapy regimens, particularly in countries with low HDI [28]. However, our study found that the OC and UC mortality rate in high HDI countries is higher than in low HDI countries because the absolute number of deaths is largely proportional to the incidence of the disease, therefore, developed countries tend to demonstrate improved mortality/incidence ratios due to advancements in detection and treatment. In general, low HDI have intermediate to low absolute mortality rates, but high mortality/incidence ratios in OC and UC [25]. In countries with a high HDI, patients with OC, UC tend to live longer because they have better access to timely diagnostic services and more effective treatment options [29]. As a result, these countries have a DALY Index compared to countries with a low HDI.

In addition, in high-HDI countries, molecular diagnostics for classifying and stratifying treatment for endometrial cancer are becoming increasingly common [30]. Tumor molecular classification in early-stage, high-risk endometrial cancer has proven to be cost-effective [31]. As a result, the prevalence rate of UC is higher in these countries. Consequently, because tumor molecular classification influences the choice of treatment, the DALY rate of UC is also higher in high-HDI countries compared to those with low HDI. However, their high costs may exacerbate inequalities if not equitably accessible.

CC is the second most common malignant tumor among females worldwide and poses a significant threat to women's health. Persistent infection with high-risk Human Papillomavirus (HPV) has been identified as the primary cause of CC, the clear etiology accelerated the establishment and implementation of comprehensive prevention and control system of CC [32]. In 2018, there were an estimated 570,000 cases of CC globally, resulting in 311,000 deaths. Globally, the incidence of CC cases increased from 335,641.56 in 1990 to 565,540.89 in 2019 [33].This makes CC the fourth most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths in women.

Notably, approximately 85% of CC deaths occur in underdeveloped or developing countries [34]. The death rate from CC is 18 times higher in low-income and middle-income countries compared to wealthier nations [35]. Consistent with the above, our ecological study showed that the incidence and mortality rates of CC are higher in countries with low HDI. The 20% of the world's population with the lowest HDI had an incidence and mortality rate for CC that accounted for approximately 35% of global cases. In contrast, the same percentage of the population with the highest HDI represented only about 0% of CC cases.

On November 17, 2020, the WHO released a global strategy aimed at accelerating the elimination of CC as a public health problem. This marked a historic commitment, with 194 countries pledging to work together to eradicate CC for the first time. According to the WHO strategy, countries must maintain an incidence rate below 4 cases per 100,000 women by achieving the 90–70–90 targets by 2030. These targets include: 90% of girls fully vaccinated with the HPV vaccine by age 15, 70% of women screened using a high-performance test by the ages of 35 and 45, and 90% of women with pre-cancerous conditions and invasive cancer receiving treatment [36], However, only 78 countries reported on HPV immunization programs, with 85% of them being high HDI countries [37].

The low incidence of CC in countries with high HDI is due to the inclusion of HPV vaccination in the general vaccination program, which has subsequently led to a decrease in positive cases of high-risk HPV strains and a decrease in positive Pap test [38]. Following the decline in incidence, mortality, and DALY rates have also decreased in these countries. Although screening rates for CC are lower in low HDI countries compared to high HDI countries, the limited coverage of HPV vaccination in low HDI countries results in a higher number of positive PAP test results, which contributes to an increased incidence of CC [38]. Furthermore, low health literacy among women in these regions often leads to delayed referrals and diagnoses at more advanced stages of the disease. Additionally, limited access to healthcare services in low- and middle-income countries contributes to higher mortality rates from CC [37]. Implementing widespread HPV immunization could help address this inequity [37].

The standard surgical treatment for CC at stage IA2-IIA involves radical hysterectomy combined with pelvic lymphadenectomy, with or without adjuvant chemo-radiation [39]. This treatment results in the loss of fertility for women of reproductive age and an increased DALY. However, with the development of minimally invasive surgical techniques such as vaginal trachelectomy and robotic-assisted radical trachelectomy, fertility can be preserved and DALY rates can be reduced in these patients [40].

At last, we suspect that the COVID-19 pandemic may have affected our study results. In general, this pandemic has a significant impact on cancer incidence and mortality, including breast, cervical, ovarian and uterine cancers. Travel restrictions and concerns about the spread of the virus have led to a decrease in regular check-ups and cancer screenings, which can lead to late diagnosis and an increase in the incidence of these diseases. In addition, health services have been strained and many patients have delayed treatment, with a direct impact on mortality and disability-adjusted life years (DALYs). Preliminary estimates suggest that these changes are being felt particularly by vulnerable groups, who typically have less access to health services, and may lead to an increased burden of disease in the future [41].

Our study had some limitations. First, since this is an ecological study, the exposure data is presented at an aggregate level. This requires us to exercise caution when interpreting the results to avoid ecological fallacy. Ecological fallacy occurs when a relationship observed at a group level does not accurately represent the association at an individual level. Another limitation of ecological studies is the difficulty in controlling for confounding variables. In addition, data gaps in cancer registries, particularly in low-HDI countries, may bias RCI estimations. This is especially true for cancers with a high risk of underreporting, such as cervical and uterine cancer. Due to these limitations, ecological studies are less effective for conclusively testing causal hypotheses. However, they can be useful for generating hypotheses for further investigation.

Conclusion

This study revealed an unequal distribution of incidence and mortality rates for women's cancers between countries with high and low HDIs. Variations in biological, cultural, and social factors, along with disparities in access to healthcare, diagnosis, screening, and treatment, may account for these regional discrepancies. Additionally, the quantity and quality of cancer registration and reporting systems differ based on HDI levels. These findings suggest that prevention, early detection, and public health education programs and policies should be tailored to reduce global cancer disparities among women, particularly in countries with high concentrations of such cases. Interventions such as expanding HPV vaccination, subsidizing molecular testing, and providing international training on management information systems could effectively reduce the identified inequalities. In the future, individual-level studies employing various analytical methods should explore the relationship between socioeconomic status and cancer risk and mortality.

Abbreviations

BC

Breast Cancer

CC

Cervical Cancer

OC

Ovarian Cancer

UC

Uterine Cancer

DALY

Disability-Adjusted Life Year’s

HDI

Human Development Index

WHO

World Health Organization

RCI

Relative Concentration Index

HPV

Human Papillomavirus

Authors’ contributions

Conceptualization: AS, FS, and EJ; Data curation: AS and RG, Software: FS; Writing—original draft: AS, FS; Writing review, and editing: All authors.

Funding

This research project is a collaborative effort between the National Institute for Medical Research Development (Grant ID: 4020662 and Ethical ID: IR.NIMAD.REC.1404.046) and the Vice-Chancellor of Research and Technology of Hamadan University of Medical Sciences (Ethical ID: IR.UMSHA.REC.1404.248). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

Data availability

The data are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This research study received ethical approval from the Ethics Committees of both the National Institute for Medical Research Development (NIMAD) and Hamadan University of Medical Sciences. The ethics approval codes for the respective institutions are as follows: [IR.NIMAD.REC.1404.046] for NIMAD and [IR.UMSHA.REC.1404.248] for Hamadan University of Medical Sciences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.