Geospatial and temporal variation of prostate cancer incidence

Socioeconomic disadvantage and long-term survival duration in out-of-hospital cardiac arrest patients: A population-based cohort study

Background

Socioeconomic status (SES) is a well-established determinant of cardiovascular health. However, the relationship between SES and clinical outcomes in long-term out-of-hospital cardiac arrest (OHCA) is less well-understood. The Singapore Housing Index (SHI) is a validated building-level SES indicator. We investigated whether SES as measured by SHI is associated with long-term OHCA survival in Singapore.

Methods

We conducted an open cohort study with linked data from the Singapore Pan-Asian Resuscitation Outcomes Study (PAROS), and the Singapore Registry of Births and Deaths (SRBD) from 2010 to 2020. We fitted generalized structural equation models, calculating hazard ratios (HRs) using a Weibull model. We constructed Kaplan-Meier survival curves and calculated the predicted marginal probability for each SHI category.

Results

We included 659 cases. In both univariable and multivariable analyses, SHI did not have a significant association with survival. Indirect pathways of SHI mediated through covariates such as Emergency Medical Services (EMS) response time (HR of low-medium, high-medium and high SHI when compared to low SHI: 0.98 (0.88-1.10), 1.01 (0.93-1.11), 1.02 (0.93-1.12) respectively), and age of arrest (HR of low-medium, high-medium and high SHI when compared to low SHI: 1.02 (0.75-1.38), 1.08 (0.84-1.38), 1.18 (0.91-1.54) respectively) had no significant association with OHCA survival. There was no clear trend in the predicted marginal probability of survival among the different SHI categories.

Conclusions

We did not find a significant association between SES and OHCA survival outcomes in residential areas in Singapore. Among other reasons, this could be due to affordable healthcare across different socioeconomic classes.

Bayesian Spatio-Temporal Multilevel Modelling of Patient-Reported Quality of Life following Prostate Cancer Surgery

BACKGROUND

Globally, prostate cancer is the second leading cause of cancer deaths among males. It is the most commonly diagnosed cancer in Australia. The quality of life of prostate cancer patients is poorer when compared to the general population due to the disease itself and its related complications. However, there is limited research on the geographic pattern of quality of life and its risk factors in Victoria. Therefore, an examination of the spatio-temporal pattern and risk factors of poor quality of life, along with the impact of spatial weight matrices on estimates and model performance, was conducted.

METHOD

A retrospective study was undertaken based on the Prostate Cancer Outcome Registry-Victoria data. Patient data (n = 5238) were extracted from the Prostate Cancer Outcome Registry, a population-based clinical quality outcome assessment from 2015 to 2021. A Bayesian spatio-temporal multilevel model was fitted to identify risk factors for poor quality of life. This study also evaluated the impact of distance- and adjacency-based spatial weight matrices. Model convergence was assessed using Gelman-Rubin statistical plots, and model comparison was based on the Watanabe-Akaike Information Criterion.

RESULTS

A total of 1906 (36.38%) prostate cancer patients who had undergone surgery experienced poor quality of life in our study. Belonging to the age group between 76 and 85 years (adjusted odds ratio (AOR) = 2.90, 95% credible interval (CrI): 1.39, 2.08), having a prostate-specific antigen level between 10.1 and 20.0 (AOR = 1.33, 95% CrI: 1.12, 1.58), and being treated in a public hospital (AOR = 1.35, 95% CrI: 1.17, 1.53) were significantly associated with higher odds of poor quality of life. Conversely, residing in highly accessible areas (AOR = 0.60, 95% CrI: 0.38, 0.94) was significantly associated with lower odds of poor prostate-specific antigen levels. Variations in estimates and model performance were observed depending on the choice of spatial weight matrices.

CONCLUSION

Belonging to an older age group, having a high prostate-specific antigen level, receiving treatment in public hospitals, and remoteness were statistically significant factors linked to poor quality of life. Substantial spatio-temporal variations in poor quality of life were observed in Victoria across local government areas. The distance-based weight matrix performed better than the adjacency-based matrix. This research finding highlights the need to reduce geographical disparities in quality of life. The statistical methods developed in this study may also be useful to apply to other population-based clinical registry settings.

Health Burdens and SES in Alabama: Using Geographic Information System to Examine Prostate Cancer Health Disparity

Simple Summary

The focus of the research was to examine the relationship between Socioeconomic status and prostate cancer in Alabama’s Black Belt region. The cancer rate in Alabama is high, and the state has one of the highest rates of prostate cancer in the USA. The research aims to identify probable reasons, raise awareness, and propose cancer prevention policies. The Geographic Information System, a robust technology, has been adopted to understand Alabama’s county-level prostate cancer incidence and mortality and its association with socioeconomic and health disparities. The analysis indicated an apparent socioeconomic disparity between the Black Belt and Non-Black Belt counties of Alabama. The poverty rate is higher in Black Belt counties. The data revealed that the preexisting condition of diabetes and obesity is closely associated with prostate cancer. Also, incidence and mortality disparities strongly relate to socioeconomic status, and the preexisting condition of obesity and diabetes adds to prostate cancer incidences. Poverty is the root course of inequalities in education, income, and healthcare facilities, particularly among African Americans, contributing to Alabama’s health burden of prostate cancer. The study proposes effective health policy intervention to prevent and reduce prostate cases and mortality among underserved communities in Alabama.

Abstract

Socioeconomic disparities influence the risk of many diseases, including cancer. The cancer rate in Alabama is high, and the state has one of the highest rates of prostate cancer in the USA. Alabama’s counties are embedded with socioeconomic disparities, politics, race, ethnicity, and oppression, among which social equity and socioeconomic status (SES) been closely associated with prostate cancer. The Geographic Information System (GIS) has become a valuable technology in understanding public health in many applications, including cancer. This study integrates Alabama’s county-level prostate cancer incidence and mortality and its association with socioeconomic and health disparities. We conducted robust data mining from several data sources such as the Alabama State Cancer Profile data, Alabama Department of Health, American Cancer Society, Center for Disease Control, and National Cancer Institute. The research method is the Geographic Information System (GIS), and we employed prostate cancer data within GIS to understand Alabama’s prostate cancer prevalence regarding SES. The GIS analysis indicated an apparent socioeconomic disparity between the Black Belt and Non-Black Belt counties of Alabama. The Black Belt counties’ poverty rate is also remarkably higher than non-Black Belt counties. In addition, we analyzed the median household income by race. Our analysis demonstrates that the Asian background population in the state earned the highest median income compared to non-Hispanic whites and the African American population. Furthermore, the data revealed that the preexisting condition of diabetes and obesity is closely associated with prostate cancer. The GIS analysis suggests that prostate cancer incidence and mortality disparities are strongly related to SES. In addition, the preexisting condition of obesity and diabetes adds to prostate cancer incidences. Poverty also reflects inequalities in education, income, and healthcare facilities, particularly among African Americans, contributing to Alabama’s health burden of prostate cancer.

Periodic trends in geographical variation of prostate cancer incidence and mortality in Finland between 1985 and 2019

BACKGROUND

Evaluation of regional variation of prostate cancer (PCa) incidence and PCa-specific mortality is essential in the assessment of equity in a national healthcare system. We evaluated PCa incidence and PCa-specific mortality between different municipalities and hospital districts in Finland over 1985-2019.

MATERIAL AND METHODS

Men diagnosed with PCa in Finland from 1985 through 2019 were retrieved from Finnish Cancer Registry. Age-standardized PCa incidence and mortality rates were estimated by municipality and hospital district as well as municipality urbanization, education, and income level using hierarchical Bayesian modeling. Standard deviations (SD) of the regional rates were compared between periods from 1985-1989 to 2015-2019.

RESULTS

We identified 123,185 men diagnosed with any stage PCa between 1985 and 2019. SD of PCa incidence rate (per 100,000 person-years) showed that the total variation of PCa incidence between different municipalities was substantial and varied over time: from 22.2 (95% CI, 17.1-27.8) in 1985-1989 to 56.5 (95% CI, 49.8-64.5) in 2000-2004. The SD of PCa mortality rate between all municipalities was from 9.0 (95% CI, 6.6-11.8) in 2005-2009 to 2.4 (95% CI, 0.9-4.8) in 2015-2019. There was a trend toward a lower PCa-specific mortality rate in municipalities with higher education level.

DISCUSSION

Regional variation in the incidence rate of PCa became more evident after initiation of PSA testing in Finland, which indicates that early diagnostic practice (PSA testing) of PCa has been different in different parts of the country. Variation in the national PCa mortality rate was indeed recognizable, however, this variation diminished at the same time as the mortality rate declined in Finland. It seems that after the initiation period of PSA testing, PSA has equalized PCa mortality outcomes in Finland.

A Bayesian spatio-temporal analysis of mortality rates in Spain: application to the COVID-19 2020 outbreak

BACKGROUND

The number of deaths attributable to COVID-19 in Spain has been highly controversial since it is problematic to tell apart deaths having COVID as the main cause from those provoked by the aggravation by the viral infection of other underlying health problems. In addition, overburdening of health system led to an increase in mortality due to the scarcity of adequate medical care, at the same time confinement measures could have contributed to the decrease in mortality from certain causes. Our aim is to compare the number of deaths observed in 2020 with the projection for the same period obtained from a sequence of previous years. Thus, this computed mortality excess could be considered as the real impact of the COVID-19 on the mortality rates.

METHODS

The population was split into four age groups, namely: (< 50; 50-64; 65-74; 75 and over). For each one, a projection of the death numbers for the year 2020, based on the interval 2008-2020, was estimated using a Bayesian spatio-temporal model. In each one, spatial, sex, and year effects were included. In addition, a specific effect of the year 2020 was added ("outbreak"). Finally, the excess deaths in year 2020 were estimated as the count of observed deaths minus those projected.

RESULTS

The projected death number for 2020 was 426,970 people, the actual count being 499,104; thus, the total excess of deaths was 72,134. However, this increase was very unequally distributed over the Spanish regions.

CONCLUSION

Bayesian spatio-temporal models have proved to be a useful tool for estimating the impact of COVID-19 on mortality in Spain in 2020, making it possible to assess how the disease has affected different age groups accounting for effects of sex, spatial variation between regions and time trend over the last few years.

Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia

Predicting lung cancer cases at the small-area level is helpful to quantify the lung cancer burden for health planning purposes at the local geographic level. Using Victorian Cancer Registry (2001-2018) data, this study aims to forecast lung cancer counts at the local government area (LGA) level over the next ten years (2019-2028) in Victoria, Australia. We used the Age-Period-Cohort approach to estimate the annual age-specific incidence and utilised Bayesian spatio-temporal models that account for non-linear temporal trends and area-level risk factors. Compared to 2001, lung cancer incidence increased by 28.82% from 1353 to 1743 cases for men and 78.79% from 759 to 1357 cases for women in 2018. Lung cancer counts are expected to reach 2515 cases for men and 1909 cases for women in 2028, with a corresponding 44% and 41% increase. The majority of LGAs are projected to have an increasing trend for both men and women by 2028. Unexplained area-level spatial variation substantially reduced after adjusting for the elderly population in the model. Male and female lung cancer cases are projected to rise at the state level and in each LGA in the next ten years. Population growth and an ageing population largely contributed to this rise.