Spatial gender-age-period-cohort analysis of pancreatic cancer mortality in Spain (1990-2013)

Has the trend in pancreatic cancer mortality in Spain started to slow down?

INTRODUCTION

the aim of this study was to describe the trends of pancreatic cancer mortality by autonomous communities (ACs) and gender in Spain (1980-2021).

METHODS

an ecological trend study was performed (with aggregated data obtained from the National Institute of Statistics). Age-standardized mortality rates (ASMRs) for pancreatic cancer (per 100,000) were estimated by direct standardization, using the European standard population. Trends in ASMR (all ages and truncated 35-64 years) were analyzed by sex in each AC using a joinpoint regression model. The annual percent changes (APC) and average annual percentage of change (AAPC) were computed for trends using the joinpoint regression analysis.

RESULTS

in both sexes, ASMRs (all ages) increased significantly (p < 0.05) during the study period (AAPC: 1.5 % in males and 1.8 % in females). The joinpoint analysis identified a turning point in the trends in the late 1980s, which delineates two periods: an initial period of significant increase followed by a period of slowing of the increase (APC: 0.9 % and 1.4 % in males and females respectively; p < 0.05). In both sexes, a significant increase in ASMR (all ages) was observed in all ACs, except in Navarre, where the rates remained stable in males. In males, three ACs (Galicia, Madrid and Navarre) showed a point of inflexion in the time trend around the year 2000 (1999, 2000 and 2001 respectively), when the rates, after a period of significant increase (ACs: 2.6 %, 2.4 %, and 2.4 %, respectively; p < 0.05), stabilized (Galicia and Navarre) or the increase slowed (Madrid). In females, only Madrid showed a point of inflection in 1992, when, after a significant increase, the rates slowed down (1992-2021; APC: 1.5 %; p < 0.05).  Conclusions: the upward trend in pancreatic cancer mortality in some ACs seems to have slowed (in both sexes in Madrid), stabilized (in men in Galicia and Navarre) or turned around (in men aged 30-64 in Navarre).

A scalable approach for short-term disease forecasting in high spatial resolution areal data

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.

Pancreatic cancer challenge in 52 Asian countries: age-centric insights and the role of modifiable risk factors (1990-2019)

Background

Pancreatic cancer is renowned for its elevated incidence and mortality rates on a global scale. The disease burden of pancreatic cancer is anticipated to increase, particularly in Asia, due to its vast and rapidly aging population.

Methods

Data from the Global Burden of Disease 2019 were analyzed for pancreatic cancer burden across 52 countries in Asia, including the incidence, mortality, and disability-adjusted life years (DALY) for pancreatic cancer, with a focus on risk factors such as high body mass index (BMI), elevated fasting plasma glucose, and smoking. We applied the Estimated Annual Percentage Change, the Age-Period-Cohort model, and decomposition analysis to evaluate incidence trends and effects.

Results

From 1990 to 2019, both incidence and mortality rates of pancreatic cancer in Asia significantly increased, with an average annual standardized incidence rate change of 1.73%. Males consistently exhibited higher rates than females, with smoking as a key risk factor. Central Asia reported the highest rates, and South Asia the lowest. The incidence rose with age, peaking in those aged 70~74. The disease burden increased in all age groups, particularly in populations aged 55 and above, representing 84.41% of total cases in 2019, up from 79.01% in 1990. Pancreatic cancer ranked the fifth in incidence among six major gastrointestinal tumors but presented a significant growth rate of mortality and DALY.

Conclusion

With the growing, aging population in Asia, the pancreatic cancer burden is projected to escalate, bringing a significant public health challenge. Hence, comprehensive public health strategies emphasizing early detection, risk modification, and optimized treatment of pancreatic cancer are imperative.

Spatial age-period-cohort analysis of hepatitis B risk in Xinjiang from 2006 to 2019

Objective

The objective of this study was to investigate the spatio-temporal distribution and epidemiological characteristics of hepatitis B in 96 districts and counties of Xinjiang and to give useful information for hepatitis B prevention and treatment.

Methods

Based on the incidence data of hepatitis B in 96 districts and counties of Xinjiang from 2006 to 2019, the global trend analysis method was used to characterize the spatial variability of the disease, and the spatial autocorrelation and spatio-temporal aggregation analysis were used to explore the spatial clustering of hepatitis B and to identify high-risk areas and periods. The Integrated Nested Laplace Approximation (INLA)-based spatial age-period-cohort model was established to further explore the influence of age, period, birth queue effect, and spatial distribution on the incidence risk of hepatitis B, and sum-to-zero constraint was adopted to avoid the issue of model unrecognition.

Results

The risk of hepatitis B in Xinjiang is increasing from west to east and from north to south, with spatial heterogeneity and spatio-temporal scanning statistics yielding five clustering areas. The spatial age-period-cohort model showed two peaks in the average risk of hepatitis B, at [25,30) years old and [50,55) years old, respectively. The mean risk of hepatitis B incidence fluctuated up and down around 1 with time, and the average risk of disease by birth cohort displayed an increasing-decreasing-stabilizing trend. Taking age, period, and cohort effect into consideration, it was found that the areas with a high risk of hepatitis B are Tianshan District, Xinshi District, Shuimogou District, Changji City, Aksu City, Kashi City, Korla City, Qiemo County and Yopurga County in Xinjiang. According to the spatio-temporal effect item, it was found that there are unobserved variables affecting the incidence of hepatitis B in some districts and counties of Xinjiang.

Conclusion

The spatio-temporal characteristics of hepatitis B and the high-risk population needed to be taken into attention. It is suggested that the relevant disease prevention and control centers should strengthen the prevention and control of hepatitis B among young people while paying attention to middle-aged and older adult people, and strengthening the prevention and monitoring of high-risk areas.

Penalized smoothing splines resolve the curvature identifiability problem in age-period-cohort models with unequal intervals

Age-period-cohort (APC) models are frequently used in a variety of health and demographic-related outcomes. Fitting and interpreting APC models to data in equal intervals (equal age and period widths) is nontrivial due to the structural link between the three temporal effects (given two, the third can always be found) causing the well-known identification problem. The usual method for resolving the structural link identification problem is to base a model on identifiable quantities. It is common to find health and demographic data in unequal intervals, this creates further identification problems on top of the structural link. We highlight the new issues by showing that curvatures which were identifiable for equal intervals are no longer identifiable for unequal data. Furthermore, through extensive simulation studies, we show how previous methods for unequal APC models are not always appropriate due to their sensitivity to the choice of functions used to approximate the true temporal functions. We propose a new method for modeling unequal APC data using penalized smoothing splines. Our proposal effectively resolves the curvature identification issue that arises and is robust to the choice of the approximating function. To demonstrate the effectiveness of our proposal, we conclude with an application to UK all-cause mortality data from the Human mortality database.

Using mortality to predict incidence for rare and lethal cancers in very small areas

Incidence and mortality figures are needed to get a comprehensive overview of cancer burden. In many countries, cancer mortality figures are routinely recorded by statistical offices, whereas incidence depends on regional cancer registries. However, due to the complexity of updating cancer registries, incidence numbers become available 3 or 4 years later than mortality figures. It is, therefore, necessary to develop reliable procedures to predict cancer incidence at least until the period when mortality data are available. Most of the methods proposed in the literature are designed to predict total cancer (except nonmelanoma skin cancer) or major cancer sites. However, less frequent lethal cancers, such as brain cancer, are generally excluded from predictions because the scarce number of cases makes it difficult to use univariate models. Our proposal comes to fill this gap and consists of modeling jointly incidence and mortality data using spatio-temporal models with spatial and age shared components. This approach allows for predicting lethal cancers improving the performance of individual models when data are scarce by taking advantage of the high correlation between incidence and mortality. A fully Bayesian approach based on integrated nested Laplace approximations is considered for model fitting and inference. A validation process is also conducted to assess the performance of alternative models. We use the new proposals to predict brain cancer incidence rates by gender and age groups in the health units of Navarre and Basque Country (Spain) during the period 2005-2008.

Sex differences in the diagnosis, treatment and prognosis of cancer: the rationale for an individualised approach

BACKGROUND

Precision medicine in oncology aims to identify the most beneficial interventions based on a patient's individual features and disease. However, disparities exist when providing cancer care to patients based on an individual's sex.

OBJECTIVE

To discuss how sex differences impact the epidemiology, pathophysiology, clinical manifestations, disease progression, and response to treatment, with a focus on data from Spain.

RESULTS

Genetic and environmental factors (social or economic inequalities, power imbalances, and discrimination) that contribute to these differences adversely affect cancer patient health outcomes. Increased health professional awareness of sex differences is essential to the success of translational research and clinical oncological care.

CONCLUSIONS

The Sociedad Española de Oncología Médica created a Task Force group to raise oncologists' awareness and to implement measures to address sex differences in cancer patient management in Spain. This is a necessary and fundamental step towards optimizing precision medicine that will benefit all individuals equally and equitably.

Survival Outcomes Among Pancreatic Cancer Patients at Kenyatta National Hospital

PURPOSE

Mortality from pancreatic cancer has risen fast in the past two decades in East Africa, including Kenya. However, there was a paucity of conclusive data about the survival of pancreatic cancer patients in the study setting. Hence, this study aimed to assess the survival outcomes of pancreatic cancer patients at Kenyatta National Hospital.

METHODS

A hospital-based retrospective cohort analysis was used to evaluate the survival outcomes among pancreatic cancer patients treated in the study setting from 1 January 2015 to 31 December 2019. A total of 64 eligible pancreatic cancer patients were included in the study. In the pre-designed data abstraction tool, the data were collected by reviewing the medical records of the patients. The data were analyzed using the Statistical Package for the Social Sciences version 22 software. The mean survival time was estimated using Kaplan-Meier survival analysis. Cox regression analysis was employed to estimate the predictors of mortality among pancreatic cancer patients.

RESULTS

The mean age of the study participants was 60.38 ± 12.61 years. Most of the patients had adenocarcinoma (96.9%) and were diagnosed at an advanced stage of the disease. The overall mean and median survival estimate for pancreatic cancer was 48.7 ± 9.7 and 39.0 ± 23.9 months, respectively. The present study showed that the overall survival rate of pancreatic cancer patients was 79.7%.

CONCLUSION

The mortality rate of pancreatic cancer in the present study was 20%. The overall mean survival estimate for pancreatic cancer was 48.7 ± 9.7 months, and the majority had disease progression in the last follow-up period.

Spatiotemporal Distribution of HIV Self-testing Kits Purchased on the Web and Implications for HIV Prevention in China: Population-Based Study

BACKGROUND

HIV self-testing (HIVST) holds great promise for expanding HIV testing. Nonetheless, large-scale data on HIVST behavior are scant. Millions of HIVST kits are sold through e-commerce platforms each year.

OBJECTIVE

This study aims to analyze the spatiotemporal distribution of the HIVST kit-purchasing population (HIVSTKPP) in China.

METHODS

Deidentified transaction data were retrieved from a leading e-commerce platform in China. A joinpoint regression model was used to examine annual trends of the HIVSTKPP rates by calculating average annual percentage change. Bayesian spatiotemporal analysis was performed to locate hot spots with HIVSTKPP rates. Spatial autocorrelation analysis and space-time cluster analysis were conducted to identify clusters of HIVSTKPP. High-high clusters of HIVSTKPP can be identified by spatial autocorrelation analysis, and high-high clusters indicate that a region and its surrounding region jointly had a higher-than-average HIVSTKPP rate. Spatial regression analysis was used to elucidate the association between the number of HIV testing facilities, urbanization ratio (the proportion of urban population in the total population), and gross domestic product per capita and the HIVSTKPP.

RESULTS

Between January 1, 2016, and December 31, 2019, a total of 2.18 million anonymous persons in China placed 4.15 million orders and purchased 4.51 million HIVST kits on the web. In each of these 4 years, the observed monthly size of the HIVSTKPP peaked in December, the month of World AIDS Day. HIVSTKPP rates per 100,000 population significantly increased from 20.62 in 2016 to 64.82 in 2019 (average annual percentage change=48.2%; P<.001). Hot spots were mainly located in municipalities, provincial capitals, and large cities, whereas high-high clusters and high-demand clusters were predominantly detected in cities along the southeast coast. We found positive correlations between a region's number of HIV testing facilities, urbanization ratio, and gross domestic product per capita and the HIVSTKPP.

CONCLUSIONS

Our study identified key areas with larger demand for HIVST kits for public health policy makers to reallocate resources and optimize the HIV care continuum. Further research combining spatiotemporal patterns of HIVST with HIV surveillance data is urgently needed to identify potential gaps in current HIV-monitoring practices.

Sex, Gender and Age Differences in Treatment Allocation and Survival of Patients With Metastatic Pancreatic Cancer: A Nationwide Study

Background

Biological sex, gender and age have an impact on the incidence and outcome in patients with metastatic pancreatic cancer. The aim of this study is to investigate whether biological sex, gender and age are associated with treatment allocation and overall survival (OS) of patients with metastatic pancreatic cancer in a nationwide cohort.

Methods

Patients with synchronous metastatic pancreatic cancer diagnosed between 2015 and 2019 were selected from the Netherlands Cancer Registry (NCR). The association between biological sex and the probability of receiving systemic treatment were examined with multivariable logistic regression analyses. Kaplan Meier analyses with log-rank test were used to describe OS.

Results

A total of 7470 patients with metastatic pancreatic cancer were included in this study. Fourty-eight percent of patients were women. Women received less often systemic treatment (26% vs. 28%, P=0.03), as compared to men. Multivariable logistic regression analyses with adjustment for confounders showed that women ≤55 years of age, received more often systemic treatment (OR 1.82, 95% CI 1.24-2.68) compared to men of the same age group. In contrast, women at >55 years of age had a comparable probability to receive systemic treatment compared to men of the same age groups. After adjustment for confounders, women had longer OS compared to men (HR 0.89, 95% CI 0.84-0.93).

Conclusion

This study found that women in general had a lower probability of receiving systemic treatment compared to men, but this can mainly be explained by age differences. Women had better OS compared to men after adjustment for confounders.

Sex Differences in the Exocrine Pancreas and Associated Diseases

Differences in pancreatic anatomy, size, and function exist in men and women. The anatomical differences could contribute to the increase in complications associated with pancreatic surgery in women. Although diagnostic criteria for pancreatitis are the same in men and women, major sex differences in etiology are reported. Alcohol and tobacco predominate in men, whereas idiopathic and obstructive etiologies predominate in women. Circulating levels of estrogens, progesterone, and androgens contribute significantly to overall health outcomes; premenopausal women have lower prevalence of cardiovascular and pancreatic diseases suggesting protective effects of estrogens, whereas androgens promote growth of normal and cancerous cells. Sex chromosomes and gonadal and nongonadal hormones together determine an individual's sex, which is distinct from gender or gender identity. Human pancreatic disease etiology, outcomes, and sex-specific mechanisms are largely unknown. In rodents of both sexes, glucocorticoids and estrogens from the adrenal glands influence pancreatic secretion and acinar cell zymogen granule numbers. Lack of corticotropin-releasing factor receptor 2 function, a G protein-coupled receptor whose expression is regulated by both estrogens and glucocorticoids, causes sex-specific changes in pancreatic histopathology, zymogen granule numbers, and endoplasmic reticulum ultrastructure changes in acute pancreatitis model. Here, we review existing literature on sex differences in the normal exocrine pancreas and mechanisms that operate at homeostasis and diseased states in both sexes. Finally, we review pregnancy-related pancreatic diseases and discuss the effects of sex differences on proposed treatments in pancreatic disease.

A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality

OBJECTIVES

This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality.

METHODS

This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018.

RESULTS

A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization.

CONCLUSIONS

Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.

Spatial Epidemiology of Pancreatic Cancer in South Dakota

BACKGROUND

Pancreatic cancer is the fourth leading cause of cancer-related mortality in the United States. Descriptive epidemiology of pancreatic cancer in South Dakota has not been studied before.

MATERIALS AND METHODS

All cases of pancreatic cancer reported to the SD Cancer Registry between January 2005 and December 2014 were included in the study. Variables collected included demographics, geographical location including county of residence and zip codes, date of diagnosis, date of last contact or follow-up, size and location of the tumor, grade of the tumor, diagnostic modality as well as therapeutic interventions. Log rank test was used to compare survival curves. Kaplan-Meier product limit estimates were provided. Data was analyzed using SAS 9.1.4 software.

RESULTS

One thousand sixty-four cases of pancreatic cancer were reported. Median age was 73 years. Cumulative age-adjusted incidence rate for pancreatic cancer for 2005-2014 was 11.1. Cumulative age-adjusted mortality rate for pancreatic cancer for 2005-2014 was 10.2. Almost half of these patients had distant metastasis at the time of presentation (n = 536; 50.4%). Overall, median survival was 5 months. Median survival for patients under the age of 60 years was 9.5 months as opposed to median survival for patients 60 years or older which was 3.9 months. Median survival of patients with well-differentiated, moderately differentiated, and poorly differentiated tumors, was 20.8 months, 8.2 months and 6.3 months respectively (p value = 0.0017).

CONCLUSION

Incidence of pancreatic cancer in South Dakota is similar to the national trends in the United States. Age at presentation, location of tumor in pancreas, and biological behavior of tumor were all predictors of survival in patients with pancreatic cancer.

Cohort Contributions to Race- and Gender-Specific Trends in the Incidence of Hepatocellular Carcinoma in the USA

INTRODUCTION

Increasing incidence of lifelong obesity and associated nonalcoholic steatohepatitis in younger birth cohorts may have contributed to growing incidence of hepatocellular carcinoma (HCC) in the USA. Yet, the contribution of cohort effects to trends in HCC incidence is unclear.

METHODS

Using data from the Surveillance, Epidemiology, and End Results (SEER) program 1973-2013, race- and gender-specific trends in HCC incidence in the USA were decomposed using age-period-cohort (APC) modeling.

RESULTS

Among SEER registry sites included in the analysis, there were 25,532 cases of HCC diagnosed including 15,867 (62%) White males, 3541 (14%) Black males, 5009 (20%) White females, and 1115 (4%) Black females. HCC incidence increases across periods, especially among men. Underlying this increase, APC models found significant cohort effects among White men, White women, and Black men, with rapid growth in HCC risk among cohorts born after 1940. A similar cohort trend among Black women did not reach statistical significance when compared to an age-period model.

CONCLUSIONS

Cohort-specific trends have significantly contributed to increasing HCC incidence in recent decades. The rapid increase in HCC risk among younger cohorts suggests that the incidence of HCC will continue increasing in the near future.

Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns

In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.