Abstract
Background
To assess the global burden and socioeconomic inequalities in the distribution of pancreatic cancer from 1990 to 2019.
Methods
We investigated the temporal trend of pancreatic cancer burden using Average Annual Percent Change (AAPC) from joinpoint regression analysis, decomposed trends by demographic and epidemiological factors, quantified cross-national health inequalities, and evaluated quality of care using the Quality of Care Index (QCI). Analyses were conducted by age, sex, sociodemographic index across 204 countries/territories from 1990 to 2019 using Global Burden of Disease Study 2019 data.
Results
The global age-standardized rates of incidence, prevalence, disability-adjusted life years (DALYs), and mortality significantly increased from 1990 to 2019, with an AAPC of 0.78, 0.95, 0.63, and 0.73, respectively. Countries with High, High-middle, Middle, Low-middle, and Low Socio-demographic Index (SDI)had a decreasing burden of pancreatic cancer in that order. The absolute cross-national inequality (Slope of inequality index) has risen from 1990 to 2019, and relative inequality (concentration index) for global PC remained essentially constant between 1990 and 2019. The overall global QCI score had increased slightly from 38.68 in 1990 to 47.04 in 2019, and it was marginally better in females (QCI score 52.96) than in males (46.44). The highest QCI score was observed in High SDI quintile with a score of 69.74, while the Low SDI quintile displayed the lowest QCI with 22.61. Additionally, the quality of care of young-onset PC patients is alarming.
Conclusion
There were wide disparities in the burden and care of pancreatic cancer across different SDI countries. Health policymakers should focus on these disparities and inequalities for appropriate policies and interventions.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-025-04380-4.
Keywords: Pancreatic cancer, Epidemiology, GBD, Health inequality, Quality of health care
Introduction
Pancreatic cancer (PC) is a digestive tract malignancy with a 5-year survival rate of about 5% [1]. As reported by GLOBOCAN 2020, pancreatic cancer ranks only 14th out of 36 cancers worldwide in terms of incidence, but jumps to 7th in terms of mortality [2]. Morbidity and mortality rates vary between countries and regions, with PC being more common in high-income areas [3, 4]. Meanwhile, the rising incidence of PC represents a substantial public health burden, notably for males and older adults [5, 6]. Understanding cross-country disparity in PC is crucial to developing effective policies.
Socio-demographic index (SDI), a recognized socioeconomic variable, is strongly associated with many disease burdens, including PC [7, 8, 9]. It is widely accepted that the high disease burden results from the synergistic effects of socio-demographic underdevelopment and poor performance of health systems [10, 11]. Existing studies show that countries with higher SDI levels typically experience heavier PC burdens, suggesting a worrying research outlook for PC epidemiology [12, 13]. Health disparity between countries with different SDI levels is increasingly seen as an obstacle to PC management [14, 15]. However, systematic information on the relationship between socio-demographic status and PC inequality has rarely been reported. Meanwhile, quality of care (QoC) for PC patients is an important aspect that has received increasing attention in recent years. Disparities in patients’ access to quality care, from timely diagnosis to advanced treatment options and supportive care services, contribute significantly to inequalities in PC outcomes across socio-demographic strata [16].
In this study, we conducted a comprehensive analysis of the Global Burden of Disease Study (GBD) 2019 database with the main objectives of (i) analyzing SDI-related PC health inequalities, (ii) assessing trends in PC inequalities from 1990 to 2019, and (iii) presenting the disparities in provided care among different age groups and sexes.
Methods
Data sources
The study data were retrospectively obtained from the GBD 2019 database on the Institute for Health Metrics and Evaluation (IHME) website, which incorporates data from 204 countries and territories, including surveys, vital registration, and health records, to estimate the epidemiological variables of various diseases and injuries worldwide. Methods around data gathering and performing the original GBD study could be found elsewhere [8, 17]. The GBD 2019 database provides standardized and comparable measures, including incidence, prevalence, disability-adjusted life years (DALYs), years of life lost (YLL), years lost due to disability (YLD), and mortality, to assess the impact of different diseases and risk factors across countries and over time. Socio-economic status is categorized by SDI (high, high-middle, middle, low-middle, and low) and World Bank income (WBI: high, upper-middle, lower-middle, and low).
Measurement health inequalities
To quantify socioeconomic inequalities in pancreatic cancer burden, we employed two complementary indices: the Slope Index of Inequality (SII) and the Health Concentration Index (HCI). These metrics capture both absolute and relative dimensions of inequality through distinct methodological approaches [18, 19]. SII is an absolute measure that represents the linear regression coefficient between population health outcomes and socioeconomic rank. Countries were ranked by SDI from most disadvantaged (rank = 0) to least disadvantaged (rank = 1). The SII was calculated using weighted least squares regression. The SII value represents the estimated difference in health outcomes between the theoretical extremes of the socioeconomic spectrum (rank 1 vs. rank 0). Positive SII values indicate higher burden in advantaged populations. HCI is a relative measure based on the Lorenz curve framework. It quantifies the degree of inequality as twice the area between the concentration curve and the line of equality. HCI ranges from − 1 to + 1, with positive values indicating pro-rich inequality (higher burden in high-SDI countries). The magnitude reflects both inequality intensity and population health distribution. Both indices were calculated for each health metric annually from 1990 to 2019. We followed WHO methodological standards for health inequality monitoring, with computational validation in R. Sensitivity analyses confirmed robustness to SDI categorization methods.
QCI estimation and validation
Quality of Care Index (QCI) is a composite indicator that represents the overall quality of care (Qoc). To generate the QCI, we integrated multiple primary and secondary indices from GBD 2019 and used principal component analysis (PCA). QCI derivation method was first proposed by Mohammadi et al. and verified in their QCI analyses for several diseases [16, 20–24]. Specifically, the four secondary indices in PCA analysis include the ratio of mortality to incidence, the ratio of prevalence to incidence, the ratio of YLL to YLD, and the ratio of DALY to prevalence. The first principal component from the PCA demonstrates a linear association of these secondary indices that interprets the greatest variance and obtains the most important details regarding Qoc, and is therefore scaled from 0 to 100 to represent the QCI for pancreatic cancer. Higher QCI values indicate better Qoc.
IHME has previously introduced Healthcare Access and Quality (HAQ) index as a measure of care quality and access [25]. A mixed-effect model was adopted by considering QCI as a dependent variable and inpatient healthcare utilization, outpatient healthcare utilization, PC death, and prevalence as independent variables by random effects of countries. We calculated the correlation between the HAQ index and QCI to be 0.62. Gender disparity ratio (GDR) was derived by QCI for females divided by QCI for males to investigate the gender differences in QoC of PC at different magnitudes.
Risk factor assessment
The contribution of key modifiable risk factors (including: High body-mass index, High fasting plasma glucose, and Smoking) to pancreatic cancer burden was evaluated using GBD 2019 data. Attributable fractions (AFs) of deaths and disability-adjusted life years (DALYs) were calculated using the comparative risk assessment framework [26]. AFs represent the proportion of burden attributable to each risk factor after adjusting for confounding [27]. GBD’s methodology integrates risk exposure distributions, relative risks from meta-analyses, and theoretical minimum risk exposure levels.
Statistical analysis
The association between SDI and age-standardized rates at different measures was explored, and smoothing spline regression analysis was identified as a common method to reflect this association. Average annual percentage change (AAPC), as an indicator to compare the average rate of growth or decline of a variable over a while, is a weighted average of the annual percentage change (APC) from the joinpoint model. Joinpoint regression analysis was employed to clarify trends in PC burden during 1990–2019, by calculating AAPC for different measurements due to PC based on Joinpoint Regression Program (Version 5.0.2. Statistical Research and Applications Branch, National Cancer Institute, USA) [28]. Decomposition analysis is also relevant to understanding and examining disparities in health outcomes of PC, and it was employed to disaggregate the measure change for PC based on population-level drivers, thus characterizing the impact of potential factors on the prevalence of PC since 1990 s [29].
All statistics (including data adjustment, data analyses, and graph production) were created and performed with R software (version, 4.2.3), and p < 0.05 (two-sided) were deemed statistically significant.
Ethics and STROCSS statemen CC
Ethical approval is not required for the use of anonymized, publicly available epidemiological data, and patient informed consent is not required to access and download data from the database. This work is reported in line with the strengthening the reporting of STROCSS criteria [30] (Supplemental Digital Content).
Results
Epidemiologic trends
Globally in 2019, there were 530,297 (95% UI 486,175, 573,635) incident cases, 442,101 (95% UI 405,961, 478,305) prevalent cases, 111,549,016 (95% UI 10,777,405, 12,338,912) DALY cases, and 531,107 (95% UI 491,948, 566,536) deaths (Fig. S1; Table S1). The age-standardized incidence, prevalence, DALYs, and mortality rate per 100,000 persons of PC were 6.57 (95% CI 6, 7.09), 5.43 (95% CI 4.96, 5.87), 139.61 (95% CI 130.18, 149.14), 6.62 (95% CI 6.11, 7.06), respectively (Fig. 1). Stratified by SDI, High SDI quintile tended to present the highest age-standardized prevalence, incidence, DALY, and mortality rate of PC, whereas Low SDI quintile had the lowest burden (Table 1, Table S2). The joinpoint regression analysis further identified a substantial change for PC in incidence (AAPC = 0.78; 95% CI 0.67, 0.88), prevalence (AAPC = 0.95; 95% CI 0.84, 1.05), DALYs (AAPC = 0.63 95% CI 0.54, 0.73), and mortality (AAPC = 0.73; 95% CI 0.63, 0.82) worldwide (Figure S2, Table S3). Low-middle SDI quintile showed the most bothersome trend in the burden of PC, followed by Low SDI quintile. We also discussed the key factors (population aging, population growth, and epidemiologic changes) that caused this trend (Figure S3). Globally, most of the increase in burden was attributed to population growth, then population aging, and epidemiologic changes. The impact of population aging on overall change was most evident in the High SDI quintile, whereas population growth and epidemiological change were noted in Low and Low-middle SDI quintiles, respectively. Stratified by sex, the age-standardized burden was higher in males than in females over decades. The burden of PC among males was concentrated in High and High-middle SDI quintiles, whereas the burden among females was concentrated in Low, Low-middle, and Middle SDI quintiles. Stratified by country, the highest age-standardized burden was observed in Greenland, Monaco, and United Arab Emirates in 2019 (Fig. S4). A positive correlation between age-standardized burden and SDI level was also confirmed in 22 GBD regions and 204 countries and territories (Fig. S5, S6).
Fig. 1.
Temporal trends in age-standardized incidence, prevalence, DALYs, and mortality rate of pancreatic cancer overall and by sex (female and male) and sociodemographic index (high, high-middle, middle, low-middle, and low categories) from 1990 to 2019. Note: DALY, disability-adjusted life years
Table 1.
Incidence, prevalence, DALYs, and mortality of pancreatic cancer by SDI quintile in 2019, with percentage change in age-standardized rates (ASR) from 1990 to 2019
| Incidence | Prevalence | DALYs | Mortality | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No., 2019 (95% UI) | ASR, 2019 (95% UI) | Δ ASR (1990–2019) | No., 2019 (95% UI) | ASR, 2019 (95% UI) | Δ ASR (1990–2019) | No., 2019 (95% UI) | ASR, 2019 (95% UI) | Δ ASR (1990–2019) | No., 2019 (95% UI) | ASR, 2019 (95% UI) | Δ ASR (1990–2019) | |
| High SDI | 196,919 (174831 to 215525) | 10.2 (9.1 to 11.1) | 17% (7% to 27%) | 179,600 (159641 to 197165) | 9.6 (8.6 to 10.5) | 30% (19% to 41%) | 3,564,050 (3347923 to 3733175) | 199.6 (188.7 to 208.4) | 8% (4% to 12%) | 189,782 (171237 to 200955) | 9.6 (8.8 to 10.2) | 13% (8% to 18%) |
| High-middle SDI | 156,543 (142579 to 170422) | 7.7 (7 to 8.3) | 25% (14% to 36%) | 124,461 (113410 to 135626) | 6.1 (5.6 to 6.6) | 29% (18% to 41%) | 3,553,955 (3272690 to 3819144) | 174.4 (160.4 to 187.4) | 18% (8% to 29%) | 159,583 (146077 to 170902) | 7.8 (7.2 to 8.4) | 23% (13% to 33%) |
| Middle SDI | 117,095 (104632 to 130584) | 4.8 (4.3 to 5.3) | 65% (46% to 86%) | 91,934 (82083 to 102644) | 3.7 (3.3 to 4.1) | 79% (57% to 103%) | 2,901,059 (2595114 to 3249069) | 112.5 (100.6 to 126) | 66% (46% to 90%) | 120,021 (107035 to 134529) | 5 (4.5 to 5.6) | 75% (55% to 100%) |
| Low-middle SDI | 46,939 (42996 to 51039) | 3.5 (3.2 to 3.8) | 95% (67% to 134%) | 36,159 (33069 to 39558) | 2.6 (2.4 to 2.9) | 100% (70% to 141%) | 1,188,693 (1080190 to 1309276) | 84.2 (76.7 to 92.5) | 91% (62% to 130%) | 48,532 (44310 to 53080) | 3.8 (3.4 to 4.1) | 96% (68% to 137%) |
| Low SDI | 12,561 (11018 to 14176) | 2.5 (2.2 to 2.9) | 51% (28% to 81%) | 9752 (8524 to 11019) | 1.9 (1.6 to 2.1) | 61% (35% to 94%) | 335,996 (293809 to 382001) | 61.4 (53.9 to 69.7) | 55% (29% to 87%) | 12,946 (11336 to 14669) | 2.7 (2.4 to 3.1) | 59% (36% to 91%) |
ASR: Age-standardized rate per 100,000 population; DALYs: Disability-adjusted life-years; SDI: Sociodemographic-index; UI, Uncertainty interval. ΔASR: Percentage change in age-standardized rate from 1990 to 2019 (95% uncertainty interval)
Cross-national inequality
The absolute and relative inequalities associated with SDI in PC burdens are striking, with a significant increase in SII and a slight decrease in HCI over time (Fig. 2). The SIIs for incidence, prevalence, DALYs, and mortality in 2019 are almost twice as high as in 1990, showing a higher PC burden disproportionately concentrated in countries with higher SDI. This increase suggests that the inequality in the PC burden between high and low SDI countries amplified over three decades. By contrast, relative inequality analysis revealed that the HCI remained almost constant between 1990 and 2019, and serious relative inequities have persisted during this period.
Fig. 2.
Health inequality regression curves and concentration curves for incidence (A), prevalence (B), DALYs (D), and mortality (D) of pancreatic cancer worldwide, 1990 and 2019. Note: DALYs, disability-adjusted life-years
The Qoc landscape of PC in countries is presented in Fig. S7 and Table S4. Canada, Japan, Germany, Finland, and Singapore were the top 5 countries with the greatest QCI in 2019, whereas top 5 countries were Japan, Canada, Slovakia, Finland, and Germany in 1990. Meanwhile, the lowest QCI was observed in African region. The global age-standardized QCI score increased slightly from 38.68 in 1990 to 47.04 in 2019, with females having a better QCI than males (Table 2; Fig. S8). Meanwhile, the High SDI quintile had the highest QCI in 2019 (69.74), while the Low SDI quintile had the lowest QCI of 22.61 (Fig. S8A). Moreover, the QCI score for High SDI quintile rose the fastest, from 47.5 in 1990 to 51.79, while the QCI scores for other SDI quintiles stayed relatively stable over the period. Consistent with SDI quintiles, High-income countries have the highest QCI (66.95) in 2019 based on the WBI level, while low-income countries still have the lowest QCI (23.08), despite showing a sharp increase (Figure S8B).
Table 2.
Quality of care index (QCI) and gender disparity ratio (GDR) for pancreatic cancer by region and income level, 1990 vs. 2019
| QCI | |||||
|---|---|---|---|---|---|
| Location | Year | Both | Female | Male | GDR (F/M) |
| Global | 1990 | 38.68 | 45.83 | 38.07 | 1.2 |
| 2019 | 47.04 | 52.96 | 46.44 | 1.14 | |
| Δ (1990–2019) | 21.63 | 15.57 | 21.98 | −5.26 | |
| SDI quintile | |||||
| High SDI | 1990 | 51.79 | 56.94 | 51.27 | 1.11 |
| 2019 | 69.74 | 73.41 | 67.81 | 1.08 | |
| Δ (1990–2019) | 34.66 | 28.93 | 32.26 | −2.53 | |
| High-middle SDI | 1990 | 28.28 | 36.88 | 28.19 | 1.31 |
| 2019 | 36.62 | 44.11 | 36.81 | 1.2 | |
| Δ (1990–2019) | 29.49 | 19.6 | 30.58 | −8.4 | |
| Middle SDI | 1990 | 22.45 | 30.08 | 24.03 | 1.25 |
| 2019 | 29.65 | 37 | 30.49 | 1.21 | |
| Δ (1990–2019) | 32.07 | 23 | 26.88 | −3.2 | |
| Low-middle SDI | 1990 | 21.35 | 29.59 | 22.38 | 1.32 |
| 2019 | 25.92 | 33 | 27.32 | 1.2 | |
| Δ (1990–2019) | 21.41 | 11.52 | 22.07 | −9.09 | |
| Low SDI | 1990 | 19.05 | 28.16 | 19.74 | 1.43 |
| 2019 | 22.61 | 30.8 | 23.38 | 1.32 | |
| Δ (1990–2019) | 18.69 | 9.38 | 18.44 | −7.69 | |
| WBI level | |||||
| High income | 1990 | 49.14 | 55.03 | 48.31 | 1.14 |
| 2019 | 66.95 | 71.24 | 64.8 | 1.1 | |
| Δ (1990–2019) | 36.24 | 29.46 | 34.13 | −3.51 | |
| Upper-middle income | 1990 | 22.78 | 30.8 | 24.14 | 1.28 |
| 2019 | 30.66 | 37.68 | 32.16 | 1.17 | |
| Δ (1990–2019) | 34.59 | 22.34 | 33.22 | −8.59 | |
| Lower-middle income | 1990 | 23.99 | 32.32 | 24.47 | 1.32 |
| 2019 | 27.28 | 34.33 | 28.52 | 1.2 | |
| Δ (1990–2019) | 13.71 | 6.22 | 16.55 | −9.09 | |
| Low income | 1990 | 19.96 | 29.56 | 20.19 | 1.46 |
| 2019 | 23.08 | 31.66 | 23.32 | 1.36 | |
| Δ (1990–2019) | 15.63 | 7.1 | 15.5 | −6.85 |
ASR: Age-standardized rate; GDR: Gender Disparity Ratio (Female QCI/Male QCI; >1 indicates better care for females). PC pancreatic cancer; QCI: Quality of care index; SDI: Socio-demographic index; WBI: World bank income. Δ: Percentage change from 1990 to 2019
Age-sex-specific difference
Generally, age-specific rates of incidence, prevalence, and DALYs demonstrated a rising-falling trend with age, and this trend is similar for males and females. Meanwhile, age-specific mortality rates consistently show an increasing trend with age (Fig. S9-S10). The number of incidents, prevalent, DALYs, and mortal patients also tended to increase and then decrease, although their peaks varied. While the global QCI score in 2019 was projected to be 46.17 for all ages, QoC is poorest for young adults aged 30 to 34 years old (29.31, Fig. 3A). Thereafter, the QoC rises sharply to 50.43 in the 55–59 age group, peaking in the 95 + age group (60.51). In 2019, the QCI was highest in the high SDI quintile and lowest in the low SDI quintile for all age groups, regardless of gender. The QCI of all SDI quintiles peaked in the 95 + years group, with the age group with the lowest QCI differing between SDI countries. The trend of QCI in WBI countries was similar to SDI levels (Fig. S11). The largest gender differences are mainly in African countries: Central African Republic (1.73), Eswatini (1.72), and Lesotho (1.63, Fig. S12). Globally, the GDR for all SDI quintiles stabilized above 1 from 1990 to 2019, indicating that the care for females was superior to that for males (Fig. 3B). The downward trend of QCI suggests the differences narrowed over the study period. GDR fluctuated considerably across age groups, with almost all age groups having the same GDR pattern by SDI category (Fig. 3C). The GDR aged 15–24 years was < 1 in five SDI quintiles, and GDR aged 25–69 years oscillated around 1.
Fig. 3.
Temporal trend of QCI (A) and GDR (B) for PC by sex based on SDI quintiles during 1990–2019, and age trend of GDR based on SDI quintiles in 2019 (C). The higher values of QCI indicate better quality of care regarding PC
Association with risk factors
High body-mass index (BMI), high fasting plasma glucose, and smoking were common risk factors for PC, and we discussed their contributions to PC mortality and DALYs (Fig. 4). The proportion of DALYs for PCs with high BMI and high fasting plasma glucose rose globally from 4.85% to 5.99%, respectively, in 1990 to 6.12% and 8.23% in 2019, while the proportion of smoking decreased from 26.35% to 21.07%. The PC age-standardized DALYs from high BMI increased as SDI declined, with nearly doubling in Low and Low-middle SDI quintiles over three decades. Likewise, the PC age-standardized DALYs due to smoking also increased as SDI declined, despite the proportions significantly decreasing in these five SDI quintiles. In 2019, the proportion of age-standardized DALYs from high fasting plasma glucose was highest in High (9.3%) and Low-middle SDI (8.33%) quintiles, and the age-standardized attributable proportion significantly increased in these five SDI quintiles. By contrast, the proportion of age-standardized deaths from smoking decreased during 1990–2019, with the highest decline in High SDI quintile (31.92% in 1990 to 24.54% in 2019). Among age-standardized deaths in PC, the proportions pf high BMI and high fasting plasma glucose were differentially increased, with the greatest increases in the Low-middle and High SDI quintiles. Additionally, we observed that DALYs and mortality from high BMI and high fasting plasma glucose were higher in females than in males (Fig. S13). Although PC DALYs and mortality due to smoking were concentrated in males, the burden was comparable between males and females in High and High-middle SDI quintiles.
Fig. 4.
Percentage of pancreatic cancer age-standardized death and DALYs attributable to three risk factors worldwide in 1990 and 2019. Three risk factors include smoking, high body-mass index, and high fasting plasma glucose
Discussion
We found that the burden of PC was continuously worse in different regions worldwide, from 1990 to 2019. Despite the persistence of cross-national absolute and relative inequalities in PC, the QCI showed an overall improvement in the care of PC patients during this period. The increased SII in the PC burden may reflect a widening of the gap between high and low SDIs, while the modest decrease in the HCI may represent an improvement in the overall distribution of health inequalities. High SDI countries (especially in North America and Europe) have better QCI than low SDI countries (e.g. some African countries). Also, analyses of QCI scores for different age groups show heterogeneous patterns, with females having higher QCI scores in most regions and countries, suggesting that they benefit more from healthcare facilities than males. Additionally, the differences in health risk factors and PC burden in different SDI countries are relatively evident.
The findings of this study on the growing burden of PCs worldwide are compatible with earlier estimates [5, 10, 12, 31]. This worrisome trend has taken a huge toll on the health of the global population since 1990 s, despite numerous studies calling for more effective action [32–34]. For example, PC burden has become an alarming threat in Asia, particularly in China, where the vast majority of the world’s population lives [35, 36]. This fact is more worrying given the general decline in the burden of some digestive tract cancers, such as liver and esophageal cancers [6, 37, 38]. Disease distribution and cross-country comparisons demonstrated that the burden of PC was strongly associated with socioeconomic levels, and higher SDI countries presented heavier age-standardized burdens of PC, which is in line with earlier reports [12]. Previous studies realized that lower rates of solar radiation, aging patterns, and lifestyle changes may be potential reasons [39–41], and the role of population aging was validated by this study. However, Low and Low-middle SDI countries demonstrated the sharpest increase in PC burden, and it is noteworthy to control PC development in low SDI countries promptly. Low SDI countries often face problems of environmental pollution and poor living conditions, which may increase the risk of PC [42].
We also observe that this inequality has raised in absolute terms (SII) but maintained a downward trend in relative terms (HCI) over three decades. PC burden is further concentrated in rich countries, but the gap in the PC burden between poor and wealthy countries is narrowing. PC is usually considered an elderly’ disease, and accelerated aging of populations in developed countries may be the major cause of this pattern [43–45]. Meanwhile, international organizations and NGOs have played an important role in providing medical assistance and technical support, which could be a potential explanation for the reduction in the relative disparities. Additionally, differences in the trends of absolute and relative health inequality can be illustrated by variations in the calculation methods [46].
Although age-standardized burden of PC was higher in high SDI areas, QCI was also higher in high SDI countries. Considering these findings, we conclude that care for PC is better in higher socioeconomic areas, which may be attributed to better diagnostic tools and well-developed registry systems, leading to earlier treatment of patients. Race and income status also cause PC care disparity [9, 47, 48]. Also, previous studies have shown that low educational attainment and unemployment are implicated in poorer quality of life for PC patients [49]. It is urgent to understand the socioeconomic discrepancies in the epidemiology and care of PC individuals, thus working to address these inequities. A report on the QoC of PC from Australia suggests geographic clusters of residence may affect the QoC available to PC patients [50]. Thereafter, a modified, three-round Delphi survey was conducted to develop an indicator as a strategy for quality assurance in PC care [51]. More recently, Armin et al. calculated the QCI based on GBD 2017 and observed that Japan, Australia, and the United States had the highest QCI for PC in 2017 14. This study updates the best QoC in Canada, Japan, and Germany for 2019 and confirms a significant improvement in QoC compared to 2017.
There were differences in PC care by age based on QCI scores. The greatest QoC was found in older PC patients in high SDI countries, whereas the greatest QoC was found in young adults in relatively low SDI countries. This difference can be attributed to factors such as the availability of healthcare resources, the prioritization of the healthcare system, healthcare investment, and age-specific healthcare guidelines [52, 53]. Countries with higher SDIs tend to have better healthcare infrastructure, specialized programs for the elderly, and greater healthcare investment, resulting in better healthcare services for older patients [54, 55]. Conversely, countries with lower SDI values may prioritize the health of younger adults and have limited resources for healthcare investment, resulting in lower overall healthcare quality. However, it is important to recognize that even the best QoC in low SDI countries is far inferior to the worst QoC in high SDI countries. Therefore, a reasonable comparison of QoC across age groups necessitates the use of different disease burden indices that take into account the heterogeneity between countries at different development levels. As indicated, QCI for females is significantly higher than that of males, and GDR for all SDI quintiles indicates that females experience superior healthcare in PC. It is worth pondering how to improve the QoC for PC, which is prevalent among males. The low GDR belongs to some developed countries (Canada, Japan, Denmark, Finland), and these countries are socio-economically developed and provide equal access and resources to women, thus improving women’s health security. However, Kenya’s lowest GDR may be because women face greater inequalities in education, employment, and health [56]. Gender equality is an important one of the Sustainable Development Goals (SDGs) initiated by UN, which is being considered by governmental and organizational policymakers [57].
The role of risk factors such as smoking, high fasting plasma glucose, and high BMI in the causation of PC has been reported [12, 58]. In 2019, the major risk factor for PC death and disability worldwide was Smoking in both sexes combined, with a decreasing trend from 1990. The decline in the proportion of PC cases due to smoking is similar to the decline in global smoking prevalence [26]. Higher education levels and health promotion make people in high SDI countries aware of the dangers of smoking and thus inclined to reduce smoking [59, 60]. Meanwhile, Smoking-related PC DALYs and deaths in females remain higher in high SDI countries, possibly due to the higher prevalence of female smoking in developed countries [61, 62]. Obesity and diabetes have also been associated with a high risk of PC in several studies [63, 64]. The burden of high BMI and high fasting plasma glucose in females was elevated in High SDI countries compared to in Low SDI countries, and slightly above that of males in High SDI countries. Accumulative evidence indicated that obesity levels tend to be lower in countries with lower income disparities [65]. The 2003 Health Survey for England also showed a significant correlation between low socio-economic status and obesity among women, perhaps because obesity has a more negative impact on women’s social mobility than men’s [66].
Policymakers should prioritize interventions to improve access to quality care for underserved populations, especially in low SDI countries. First, governments should integrate evidence-based screening pilot programs targeting high-risk populations (e.g., individuals with new-onset diabetes accompanied by weight loss or carriers of BRCA1/2 gene mutations) into existing non-communicable disease initiatives. This should be implemented with technical support from WHO protocols and concessional financing from global funds such as the Global Fund or World Bank. Second, regional pooled procurement mechanisms should be established to reduce costs for essential diagnostics (contrast-enhanced CT/MRI, endoscopic ultrasound) and systemic therapies (FOLFIRINOX, gemcitabine/nab-paclitaxel), thereby mitigating catastrophic out-of-pocket expenditures caused by the disease. Third, insurance reforms incorporating clearly defined essential benefit packages—covering pancreatic cancer surgery, oncologic therapeutics, and palliative care—should be implemented. These reforms should draw from models like Thailand’s Universal Coverage Scheme or Mexico’s Seguro Popular and subsequently be scaled up in other low- and middle-income countries.
Interpreting global health disparities requires full consideration of the structural inequalities in data quality across different SDI regions, which reflect systemic gaps in healthcare infrastructure and resources. Studies indicate that the lower burden of pancreatic cancer in low-SDI regions may be partly attributable to systemic underdiagnosis rather than true biological differences [40, 54]. This diagnostic gap likely results in substantial underestimation of pancreatic cancer occurrence rates and prevalence in low-SDI regions, with mortality data potentially being misattributed to nonspecific causes. In these regions, incomplete cancer registries, limited diagnostic technologies, and weak death certification mechanisms often lead to severe underestimation of incidence and prevalence rates, while mortality data may also be subject to misattribution [31]. Furthermore, inequitable distribution of medical resources exacerbates prognostic disparities. High-SDI regions benefit from multidisciplinary teams, advanced surgical techniques, chemotherapy, and targeted therapies, which significantly prolong survival. In contrast, low-SDI regions face shortages of oncology specialists, limited surgical capacity, and poor drug accessibility. These gaps in treatment not only affect survival outcomes but also deepen global health inequities [3, 12]. Significant cross-SDI differences also exist in the socioeconomic burden imposed by pancreatic cancer. While patients in high-SDI regions may experience financial strain due to out-of-pocket expenses, those in low-SDI regions are often pushed into poverty by catastrophic health expenditures [26]. Additional barriers—such as cultural obstacles, long travel distances to healthcare facilities, and competition for medical priorities in resource-limited settings—further constrain healthcare access. These multifaceted inequalities underscore the urgent need to strengthen health system capacities, improve diagnostic infrastructure, and promote equitable access to quality pancreatic cancer care worldwide.
This study provided inequality perspective on PC burden and QoC together with risk factors, which is a major advantage of this survey. There are inevitably some limitations to our study. First, as discussed earlier, substantial under-ascertainment of pancreatic cancer in low-SDI regions may bias burden estimates and inequality metrics, potentially overstating disparities. Second, ethnicities, races, or etiologies were potential determinators for PC inequality, and these factors were not included in GBD study. Additionally, QCI, as a new epidemiological indicator, cannot cover all dimensions of QoC, more indicators are needed to evaluate QoC comprehensively.
Conclusion
Our study explored cross-national inequalities and QoC for PC patients in different SDI areas, and found that disparities were associated with socioeconomic status, age, gender, and risk factors. These gaps and inequalities should be emphasized in the development of appropriate policies and actions. These findings may serve as a foundation for establishing preventive initiatives and devising a new management approach to mitigate the global burden of PC.
Supplementary Information
Author contributions
All authors contributed to the study’s conception and design. X. Q. performed data collection and analysis. X. Q., and C. X. wrote the manuscript. C. X., X. Q., and Y. L. polished and revised the manuscript. All authors commented on previous versions of the manuscript and read and approved the final manuscript.
Funding
Not applicable.
Data availability
The data used in this study can be derived from the GBD 2019 (Available at: https://ghdx.healthdata.org/gbd-2019), or the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
There are no ethical considerations applicable to our article.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Siegel RL, Giaquinto AN, Jemal A. Global temporal patterns of pancreatic cancer and association with socioeconomic development. Sci Rep. 2024;74(1):12–49. 10.3322/caac.21820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
- 3.Wong MCS, Jiang JY, Liang M, Fang Y, Yeung MS, Sung JJY. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2017;7(1):3165. 10.1038/s41598-017-02997-2. [DOI] [PubMed] [Google Scholar]
- 4.Amini M, Azizmohammad Looha M, Rahimi Pordanjani S, Asadzadeh Aghdaei H, Pourhoseingholi MA. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2023;18(7):e0288755. 10.1371/journal.pone.0288755. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Yu J, Yang X, He W, Ye W. Burden of pancreatic cancer along with attributable risk factors in Europe between 1990 and 2019, and projections until 2039. Int J Cancer. 2021;149(5):993–1001. 10.1002/ijc.33617. [DOI] [PubMed] [Google Scholar]
- 6.Arnold M, Abnet CC, Neale RE, et al. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2020;159(1):335-349.e15. 10.1053/j.gastro.2020.02.068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kan C, Liu N, Zhang K, et al. Global, Regional, and National burden of pancreatic Cancer, 1990–2019: results from the global burden of disease study 2019. Ann Glob Health. 2023;89(1):33. 10.5334/aogh.4019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.G. B. D. Diseases Injuries Collaborators. Global, regional, and national burden of pancreatic cancer, 1990–2019: results from the Global Burden of Disease Study 2019. Ann Glob Health. 2020;396(10258):1204–22. 10.1016/S0140-6736(20)30925-9. [Google Scholar]
- 9.Roth GS, Fayet Y, Benmameche-Medjahed S, et al. Structural and socio-spatial determinants influencing care and survival of patients with a pancreatic adenocarcinoma: results of the PANDAURA cohort. Cancers (Basel). 2022. 10.3390/cancers14215413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Huang J, Lok V, Ngai CH, et al. Worldwide burden of, risk factors for, and trends in pancreatic cancer. Gastroenterology. 2021;160(3):744–54. 10.1053/j.gastro.2020.10.007. [DOI] [PubMed] [Google Scholar]
- 11.Peery AF, Crockett SD, Murphy CC, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2018. Gastroenterology. 2019;156(1):254-272.e11. 10.1053/j.gastro.2018.08.063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.GBD 2017 Pancreatic Cancer Collaborators. The global, regional, and National burden of pancreatic cancer and its attributable risk factors in 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet Gastroenterol Hepatol. 2019;4(12):934–47. 10.1016/S2468-1253(19)30347-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lin L, Li Z, Yan L, Liu Y, Yang H, Li H. Global, regional, and national cancer incidence and death for 29 cancer groups in 2019 and trends analysis of the global cancer burden, 1990–2019. J Hematol Oncol. 2021;14(1):197. 10.1186/s13045-021-01213-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Aryannejad A, Tabary M, Ebrahimi N, et al. Global, regional, and national survey on the burden and quality of care of pancreatic cancer: a systematic analysis for the Global Burden of Disease study 1990–2017. Pancreatology. 2021;21(8):1443–50. 10.1016/j.pan.2021.09.002. [DOI] [PubMed] [Google Scholar]
- 15.Lu L, Mullins CS, Schafmayer C, Zeißig S, Linnebacher M. A global assessment of recent trends in gastrointestinal cancer and lifestyle-associated risk factors. Cancer Commun (Lond). 2021;41(11):1137–51. 10.1002/cac2.12220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Rashidi M-M, Saeedi Moghaddam S, Azadnajafabad S, et al. Burden and quality of care index of chronic kidney disease: Global Burden of Disease analysis for 1990–2019. Nephrol Dial Transplant. 2024;39(2):317–27. 10.1093/ndt/gfad172. [DOI] [PubMed] [Google Scholar]
- 17.GBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1160–203. 10.1016/S0140-6736(20)30977-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ordunez P, Martinez R, Soliz P, Giraldo G, Mujica OJ, Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990–2017: a population-based study. Lancet Glob Health. 2019;7(10):e1388–97. 10.1016/S2214-109X(19)30360-2. [DOI] [PubMed] [Google Scholar]
- 19.Hosseinpoor AR, Bergen N, Schlotheuber A. Promoting health equity: WHO health inequality monitoring at global and national levels. Glob Health Action. 2015;8:29034. 10.3402/gha.v8.29034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Mohammadi E, Ghasemi E, Azadnajafabad S, et al. A global, regional, and national survey on burden and Quality of Care Index (QCI) of brain and other central nervous system cancers; global burden of disease systematic analysis 1990–2017. PLoS One. 2021;16(2):e0247120. 10.1371/journal.pone.0247120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hanifiha M, Ghanbari A, Keykhaei M, et al. Global, regional, and national burden and quality of care index in children and adolescents: a systematic analysis for the global burden of disease study 1990–2017. PLoS One. 2022;17(4):e0267596. 10.1371/journal.pone.0267596. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Azadnajafabad S, Saeedi Moghaddam S, Mohammadi E, et al. Global, regional, and national burden and quality of care index (QCI) of thyroid cancer: a systematic analysis of the Global Burden of Disease Study 1990–2017. Cancer Med. 2021;10(7):2496–508. 10.1002/cam4.3823. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Momtazmanesh S, Saeedi Moghaddam S, Malakan Rad E, et al. Global, regional, and national burden and quality of care index of endocarditis: the global burden of disease study 1990–2019. Eur J Prev Cardiol. 2022;29(8):1287–97. 10.1093/eurjpc/zwab211. [DOI] [PubMed] [Google Scholar]
- 24.Aminorroaya A, Yoosefi M, Rezaei N, et al. Global, regional, and national quality of care of ischaemic heart disease from 1990 to 2017: a systematic analysis for the Global Burden of Disease Study 2017. Eur J Prev Cardiol. 2022;29(2):371–9. 10.1093/eurjpc/zwab066. [DOI] [PubMed] [Google Scholar]
- 25.GBD 2016 Healthcare Access and Quality Collaborators. Measuring performance on the healthcare access and quality index for 195 countries and territories and selected subnational locations: a systematic analysis from the global burden of disease study 2016. Lancet. 2018;391(10136):2236–71. 10.1016/S0140-6736(18)30994-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Murray CJ, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global burden of disease study 2019. Lancet. 2020;396(10258):1223–49. 10.1016/S0140-6736(20)30752-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J. Global, regional, and national comparative risk assessment of 84 behaviour environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global burden of disease study 2017. Lancet. 2018;392(10159):1923–94. 10.1016/S0140-6736(18)32225-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51. [DOI] [PubMed] [Google Scholar]
- 29.Jackson JW, VanderWeele TJ. Decomposition analysis to identify intervention targets for reducing disparities. Epidemiology. 2018;29(6):825–35. 10.1097/EDE.0000000000000901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mathew G, Agha R, Albrecht J, et al. STROCSS 2021: strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int J Surg. 2021;96:106165. 10.1016/j.ijsu.2021.106165. [DOI] [PubMed] [Google Scholar]
- 31.Xiao AY, Tan MLY, Wu LM, et al. Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies. The Lancet Gastroenterology & Hepatology. 2016;1(1):45–55. 10.1016/S2468-1253(16)30004-8. [DOI] [PubMed] [Google Scholar]
- 32.Chen J, Chen H, Zhang T, et al. Burden of pancreatic cancer along with attributable risk factors in China from 1990 to 2019, and projections until 2030. Pancreatology. 2022;22(5):608–18. 10.1016/j.pan.2022.04.011. [DOI] [PubMed] [Google Scholar]
- 33.Yu Z, Bai X, Zhou R, et al. Differences in the incidence and mortality of digestive cancer between Global Cancer Observatory 2020 and Global Burden of Disease 2019. Int J Cancer. 2024;154(4):615–25. 10.1002/ijc.34740. [DOI] [PubMed] [Google Scholar]
- 34.Cucchetti A, Johnson P, Capurso G, et al. European’ health care indicators and pancreatic cancer incidence and mortality: a mediation analysis of Eurostat data and Global Burden of Disease Study 2019. Pancreatology. 2023;23(7):829–35. 10.1016/j.pan.2023.09.001. [DOI] [PubMed] [Google Scholar]
- 35.Zhu B, Wu X, Guo T, Guan N, Liu Y. Epidemiological characteristics of pancreatic cancer in China from 1990 to 2019. Cancer Control. 2021;28:10732748211051536. 10.1177/10732748211051536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Huang J, Lucero-Prisno DE, Zhang L, et al. Updated epidemiology of gastrointestinal cancers in East Asia. Nat Rev Gastroenterol Hepatol. 2023;20(5):271–87. 10.1038/s41575-022-00726-3. [DOI] [PubMed] [Google Scholar]
- 37.Qi J, Li M, Wang L, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data. Lancet Public Health. 2023;8(12):e943–55. 10.1016/S2468-2667(23)00211-6. [DOI] [PubMed] [Google Scholar]
- 38.Oesophageal Cancer Collaborators. The global, regional, and national burden of oesophageal cancer and its attributable risk factors in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol. 2017;5(6):582–97. 10.1016/S2468-1253(20)30007-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021;18(7):493–502. 10.1038/s41575-021-00457-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Stoffel EM, Brand RE, Goggins M. Pancreatic cancer: changing epidemiology and new approaches to risk assessment, early detection, and prevention. Gastroenterology. 2023;164(5):752–65. 10.1053/j.gastro.2023.02.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Yeo TP, Lowenfels AB. Demographics and epidemiology of pancreatic cancer. Cancer J. 2012;18(6):477–84. 10.1097/PPO.0b013e3182756803. [DOI] [PubMed] [Google Scholar]
- 42.Amri F, Koulali H, Jabi R, et al. Pancreatic cancer: experience from an emerging country in North Africa. J Cancer Res Clin Oncol. 2023;149(15):14297–302. 10.1007/s00432-023-05245-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Lippi G, Mattiuzzi C. The global burden of pancreatic cancer. Arch Med Sci. 2020;16(4):820–4. 10.5114/aoms.2020.94845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Yuan C, Kim J, Wang QL, et al. The age-dependent association of risk factors with pancreatic cancer. Ann Oncol. 2022;33(7):693–701. 10.1016/j.annonc.2022.03.276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Khalaf N, El-Serag HB, Abrams HR, Thrift AP. Burden of pancreatic cancer: from epidemiology to practice. Clin Gastroenterol Hepatol. 2021;19(5):876–84. 10.1016/j.cgh.2020.02.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Dover DC, Belon AP. The health equity measurement framework: a comprehensive model to measure social inequities in health. Int J Equity Health. 2019;18(1):36. 10.1186/s12939-019-0935-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Maharaj AD, Evans SM, Ioannou LJ, et al. The association between quality care and outcomes for a real-world population of Australian patients diagnosed with pancreatic cancer. HPB (Oxford). 2022;24(6):950–62. 10.1016/j.hpb.2021.11.005. [DOI] [PubMed] [Google Scholar]
- 48.Gandy RC, Barbour AP, Samra J, et al. Refining the care of patients with pancreatic cancer: the AGITG Pancreatic Cancer Workshop consensus. Med J Aust. 2016;204(11):419–22. 10.5694/mja16.00061. [DOI] [PubMed] [Google Scholar]
- 49.Chung V, Sun V, Ruel N, Smith TJ, Ferrell BR. Improving palliative care and quality of life in pancreatic cancer patients. J Palliat Med. 2022;25(5):720–7. 10.1089/jpm.2021.0187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Burmeister EA, O’Connell DL, Jordan SJ, et al. Factors associated with quality of care for patients with pancreatic cancer in Australia. Med J Aust. 2016;205(10):459–65. 10.5694/mja16.00567. [DOI] [PubMed] [Google Scholar]
- 51.Maharaj AD, Ioannou L, Croagh D, et al. Monitoring quality of care for patients with pancreatic cancer: a modified Delphi consensus. HPB. 2019;21(4):444–55. 10.1016/j.hpb.2018.08.016. [DOI] [PubMed] [Google Scholar]
- 52.Grossberg AJ, Chu LC, Deig CR, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020;70(5):375–403. 10.3322/caac.21626. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Papageorge MV, Evans DB, Tseng JF. Health care disparities and the future of pancreatic cancer care. Surg Oncol Clin North Am. 2021;30(4):759–71. 10.1016/j.soc.2021.06.012. [DOI] [PubMed] [Google Scholar]
- 54.Cabasag CJ, Arnold M, Rutherford M, et al. Pancreatic cancer survival by stage and age in seven high-income countries (ICBP SURVMARK-2): a population-based study. Br J Cancer. 2022;126(12):1774–82. 10.1038/s41416-022-01752-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Doria C, Deyne Pde, Dolan S, et al. Municipality and adjusted gross income influence outcome of patients diagnosed with pancreatic cancer in a newly developed cancer center in Mercer County New Jersey, USA, a single center study. Cancers (Basel). 2021. 10.3390/cancers13071498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Nejadghaderi SA, Kolahi A-A, Noori M, Sullman MJM, Safiri S. The burden of pancreatic cancer and its attributable risk factors in the middle East and North Africa region, 1990–2019. J Gastroenterol Hepatol. 2023;38(9):1535–45. 10.1111/jgh.16217. [DOI] [PubMed] [Google Scholar]
- 57.Asif M, Khan PA, Irfan F, Salim M, Jan A, Khan M. Is gender diversity is diversity washing or good governance for firm sustainable development goal performance: a scoping review. Environ Sci Pollut Res Int. 2023;30(53):114690–705. 10.1007/s11356-023-30211-6. [DOI] [PubMed] [Google Scholar]
- 58.Cao F, Li F, Shi L, et al. Spatial and temporal trends in pancreatic cancer burden attributable to high body mass index at the global and national levels. J Epidemiol Glob Health. 2023;13(4):831–41. 10.1007/s44197-023-00155-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Carnazza G, Liberati P, Resce G, Molinaro S. Smoking and income distribution: inequalities in new and old products. Health Policy. 2021;125(2):261–8. 10.1016/j.healthpol.2020.11.012. [DOI] [PubMed] [Google Scholar]
- 60.Wang Q, Shen JJ, Sotero M, Li CA, Hou Z. Income, occupation and education: are they related to smoking behaviors in China? PLoS One. 2018;13(2):e0192571. 10.1371/journal.pone.0192571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Sreeramareddy CT, Pradhan PMS, Mir IA, Sin S. Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from Demographic and Health Surveys. Popul Health Metr. 2014;12:22. 10.1186/s12963-014-0022-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Ver Bilano, Gilmour S, Moffiet T, et al. Global trends and projections for tobacco use, 1990–2025: an analysis of smoking indicators from the WHO comprehensive information systems for tobacco control. Lancet. 2015;385(9972):966–76. 10.1016/S0140-6736(15)60264-1. [DOI] [PubMed] [Google Scholar]
- 63.Andersen DK, Korc M, Petersen GM, et al. Diabetes, pancreatogenic diabetes, and pancreatic cancer. Diabetes. 2017;66(5):1103–10. 10.2337/db16-1477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Sharma S, Tapper WJ, Collins A, Hamady ZZR. Predicting pancreatic cancer in the UK Biobank Cohort using polygenic risk scores and diabetes mellitus. Gastroenterology. 2022;162(6):1665-1674.e2. 10.1053/j.gastro.2022.01.016. [DOI] [PubMed] [Google Scholar]
- 65.Anekwe CV, Jarrell AR, Townsend MJ, Gaudier GI, Hiserodt JM, Stanford FC. Socioeconomics of obesity. Curr Obes Rep. 2020;9(3):272–9. 10.1007/s13679-020-00398-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Vassallo AJ, Hiller CE, Pappas E, Stamatakis E. Temporal trends in dancing among adults between 1994 and 2012: the Health Survey for England. Prev Med. 2018;106:200–8. 10.1016/j.ypmed.2017.11.005. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used in this study can be derived from the GBD 2019 (Available at: https://ghdx.healthdata.org/gbd-2019), or the corresponding author upon reasonable request.