Using geographically weighted Poisson regression to examine the association between socioeconomic factors and hysterectomy incidence in Wallonia, Belgium

Geo-spatial epidemiology of gallbladder cancer in Bihar, India

Recently, a substantial increase in gallbladder cancer (GBC) cases has been reported in Bihar, India. The region's groundwater can naturally contain harmful concentrations of arsenic, which appears to be epidemiologically linked to the unusually high incidence. However, the root causes remain largely unexplored. Recent findings of uranium in the state's groundwater may also have associations. This study investigates the geo-spatial epidemiology of GBC in Bihar, India-with a focus on the correlation between environmental carcinogens, particularly arsenic and uranium in groundwater, and the incidence of GBC. Utilizing data from 8460 GBC patients' registration records over an 11-year period at a single health center, the research employs Semi-parametric Geographically Weighted Poisson Regression (S-GWPR) to account for non-stationarity associations and explores significant factors contributing to GBC prevalence at a subdistrict level. The S-GWPR model outperformed the standard Poisson regression model. The estimates suggest that arsenic and uranium concentrations in groundwater did not present significant associations; however, this could be due to the lower resolution of this data at the district level, necessitating higher resolution data for accurate estimates. Other socio-environmental factors included demonstrated significant regional heterogeneity in their association with GBC prevalence. Notably, each 1 % increase in the coverage of well- and canal-irrigated areas is associated with a maximum of 3.0 % and 5.2 % rise in the GBC incidence rate, respectively, likely attributable to carcinogen exposure from irrigation water. Moreover, distance to the health center and domestic electricity connections appear to influence the number of reported GBC cases. The latter suggests that access to electricity might have facilitated the use of groundwater pumps-increasing exposure to carcinogens. The results underscore the necessity for targeted health policies and interventions based on fine-resolution spatial analysis, as well as ongoing environmental monitoring and research to better understand the multifaceted risk factors contributing to GBC.

Examining the Spatial Varying Effects of Sociodemographic Factors on Adult Cochlear Implantation Using Geographically Weighted Poisson Regression

OBJECTIVE

To (i) demonstrate the utility of geographically weighted Poisson regression (GWPR) in describing geographical patterns of adult cochlear implant (CI) incidence in relation to sociodemographic factors in a publicly funded healthcare system, and (ii) compare Poisson regression and GWPR to fit the aforementioned relationship.

STUDY DESIGN

Retrospective study of provincial CI Program database.

SETTING

Academic hospital.

PATIENTS

Adults 18 years or older who received a CI from 2020 to 2021.

INTERVENTIONS

Cochlear implant.

MAIN OUTCOME MEASURES

CI incidence based on income level, education attainment, age at implantation, and distance from center, and spatial autocorrelation across census metropolitan areas.

RESULTS

Adult CI incidence varied spatially across Ontario (Moran's I = 0.04, p < 0.05). Poisson regression demonstrated positive associations between implantation and lower income level (coefficient = 0.0284, p < 0.05) and younger age (coefficient = 0.1075, p < 0.01), and a negative association with distance to CI center (coefficient = -0.0060, p < 0.01). Spatial autocorrelation was significant in Poisson model (Moran's I = 0.13, p < 0.05). GWPR accounted for spatial differences (Moran's I = 0.24, p < 0.690), and similar associations to Poisson were observed. GWPR further identified clusters of implantation in South Central census metropolitan areas with higher education attainment.

CONCLUSIONS

Adult CI incidence demonstrated a nonstationary relationship between implantation and the studied sociodemographic factors. GWPR performed better than Poisson regression in accounting for these local spatial variations. These results support the development of targeted interventions to improve access and utilization to CIs in a publicly funded healthcare system.

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21-65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

Analysis of the Relationship between Socioeconomic Status and Incidence of Hysterectomy Using Data of the Korean Genome and Epidemiology Study (KoGES)

Hysterectomy remains a frequent gynecologic surgery, although its rates have been decreasing. The aim of this study was to investigate whether socioeconomic status affected the risk of hysterectomy in Korean women. This prospective cohort study used epidemiologic data from 2001 to 2016, from the Korean Genomic and Epidemiology Study (KoGES). Multivariate logistic regression analyses were performed to estimate the association between household income or education level and hysterectomy. Among 5272 Korean women aged 40−69 years, 720 who had a hysterectomy and 4552 controls were selected. Variable factors were adjusted using logistic regression analysis (adjusted model). Adjusted odds ratios (aORs) for insurance type and hysterectomy were not statistically significant. The aOR was 1.479 (95% confidence interval (CI): 1.018−2.146, p < 0.05) for women with education of high school or lower compared to college or higher. Women whose monthly household income was <KRW 4,000,000 had a higher risk of undergoing hysterectomy than women whose monthly household income was ≥KRW 4,000,000 (aOR: 2.193, 95% CI: 1.639−2.933, p < 0.001). Overall, the present study elucidated that lower socioeconomic status could increase the incidence of hysterectomy. Our results indicate that the implementation of stratified preventive strategies for uterine disease in those with low education and low income could be beneficial.