GEOGRAPHICALLY-WEIGHTED REGRESSION ANALYSIS OF PERCENTAGE OF LATE-STAGE PROSTATE CANCER DIAGNOSIS IN FLORIDA

Application of Geographic Information Systems (GIS) in the Study of Prostate Cancer Disparities: A Systematic Review

Introduction: PCa is one of the cancers that exhibits the widest disparity gaps. Geographical place of residence has been shown to be associated with healthcare access/utilization and PCa outcomes. Geographical Information Systems (GIS) are widely being utilized for PCa disparities research, however, inconsistencies in their application exist. This systematic review will summarize GIS application within PCa disparities research, highlight gaps in the literature, and propose alternative approaches. Methods: This paper followed the methods of the Cochrane Collaboration and the criteria set of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in peer-reviewed journals were searched through the PubMed, Embase, and Web of Science databases until December 2022. The main inclusion criteria were employing a GIS approach and examining a relationship between geographical components and PCa disparities. The main exclusion criteria were studies conducted outside the US and those that were not published in English. Results: A total of 25 articles were included; 23 focused on PCa measures as outcomes: incidence, survival, and mortality, while only 2 examined PCa management. GIS application in PCa disparities research was grouped into three main categories: mapping, processing, and analysis. GIS mapping allowed for the visualization of quantitative, qualitative, and temporal trends of PCa factors. GIS processing was mainly used for geocoding and smoothing of PCa rates. GIS analysis mainly served to evaluate global spatial autocorrelation and distribution of PCa cases, while local cluster identification techniques were mainly employed to identify locations with poorer PCa outcomes, soliciting public health interventions. Discussion: Varied GIS applications and methodologies have been used in researching PCa disparities. Multiple geographical scales were adopted, leading to variations in associations and outcomes. Geocoding quality varied considerably, leading to less robust findings. Limitations in cluster-detection approaches were identified, especially when variations were captured using the Spatial Scan Statistic. GIS approaches utilized in other diseases might be applied within PCa disparities research for more accurate inferences. A novel approach for GIS research in PCa disparities could be focusing more on geospatial disparities in procedure utilization especially when it comes to PCa screening techniques. Conclusions: This systematic review summarized and described the current state and trend of GIS application in PCa disparities research. Although GIS is of crucial importance when it comes to PCa disparities research, future studies should rely on more robust GIS techniques, carefully select the geographical scale studied, and partner with GIS scientists for more accurate inferences. Such interdisciplinary approaches have the potential to bridge the gaps between GIS and cancer prevention and control to further advance cancer equity.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Modelling geographical heterogeneity of diabetes prevalence and socio-economic and built environment determinants in Saudi City - Jeddah

Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.

Spatial analysis of colorectal cancer outcomes and socioeconomic factors in Virginia

BACKGROUND

Colorectal cancer (CRC) disparities vary by country and population group, but often have spatial features. This study of the United States state of Virginia assessed CRC outcomes, and identified demographic, socioeconomic and healthcare access contributors to CRC disparities.

METHODS

County- and city-level cross-sectional data for 2011-2015 CRC incidence, mortality, and mortality-incidence ratio (MIR) were analyzed for geographically determined clusters (hotspots and cold spots) and their correlates. Spatial regression examined predictors including proportion of African American (AA) residents, rural-urban status, socioeconomic (SES) index, CRC screening rate, and densities of primary care providers (PCP) and gastroenterologists. Stationarity, which assesses spatial equality, was examined with geographically weighted regression.

RESULTS

For incidence, one CRC hotspot and two cold spots were identified, including one large hotspot for MIR in southwest Virginia. In the spatial distribution of mortality, no clusters were found. Rurality and AA population were most associated with incidence. SES index, rurality, and PCP density were associated with spatial distribution of mortality. SES index and rurality were associated with MIR. Local coefficients indicated stronger associations of predictor variables in the southwestern region.

CONCLUSIONS

Rurality, low SES, and racial distribution were important predictors of CRC incidence, mortality, and MIR. Regions with concentrations of one or more factors of disparities face additional hurdles to improving CRC outcomes. A large cluster of high MIR in southwest Virginia region requires further investigation to improve early cancer detection and support survivorship. Spatial analysis can identify high-disparity populations and be used to inform targeted cancer control programming.

Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever

Environment and human mobility have been considered as two important factors that drive the outbreak and transmission of dengue fever (DF). Most studies focus on the local environment while neglecting environment of the places, especially epidemic areas that people came from or traveled to. Commuting is a major form of interactions between places. Therefore, this research generates commuting flows from mobile phone tracked data. Geographically weighted Poisson regression (GWPR) and analysis of variance (ANOVA) are used to examine the effect of commuting flows, especially those from/to epidemic areas, on DF in 2014 at the Jiedao level in Guangzhou. The results suggest that (1) commuting flows from/to epidemic areas affect the transmission of DF; (2) such effects vary in space; and (3) the spatial variation of the effects can be explained by the environment of the epidemic areas that commuters commuted from/to. These findings have important policy implications for making effective intervention strategies, especially when resources are limited.

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

Insurance status as a modifier of the association between race and stage of prostate cancer diagnosis in Florida during 1995 and 2013

BACKGROUND

Cancer stage at diagnosis is a critical prognostic factor regarding a patient's health outcomes. It has yet to be shown whether insurance status across different race has any implications on the stage of disease at the time of diagnosis. This study aimed to investigate whether insurance status was a modifier of the association between race and stage of previously undetected prostate cancer at the time of diagnosis in Florida between 1995 and 2013.

METHODS

Secondary data analysis of a cross-sectional survey using information from the Florida Cancer Data System (n = 224,819). Study participants included black and white males diagnosed with prostate cancer in Florida between 1995 and 2013. The main outcome variable was stage of prostate cancer at diagnosis. The main independent variable was race (black vs white). Adjusted logistic regression models were used to explore the association between race, insurance status and stage at diagnosis. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated.

RESULTS

Black males were more likely to be diagnosed with late stage prostate cancer (OR 1.31; 95% CI 1.27-1.35). Being uninsured (OR 2.28; 95% CI 2.13-2.45) or having Medicaid (OR 1.84; 95% CI 1.70-1.98) was associated with a diagnosis of late stage cancer. Stratified analysis for health insurance revealed that blacks had an increased risk for late stage cancer if uninsured (OR 1.29; 95% CI 1.07-1.55) and if having Medicare (OR 1.39; 95% CI 1.31-1.48).

CONCLUSION

Insurance status may modify the effect of race on late stage prostate cancer in black patients.

Analysis of Spatial Pattern Evolution and Influencing Factors of Regional Land Use Efficiency in China Based on ESDA-GWR

In order to give an in-depth understanding of the contradictions arising from the land resource supply and demand, this study selected 30 provinces (some are autonomous regions or municipalities) in China to be the research unit, used the carbon emission as an undesirable output, and adopted the Super-SBM DEA model and ESDA-GWR method to research the evolution characteristics and influencing factors of land use efficiency in China in 2003–2013. The results indicated that: (1) The land use efficiency in China overall was moderately ineffective and the overall utilization level was low; (2) The Global Spatial Autocorrelation was instable and had maintained a high level; (3) The “hot spots” mainly being distributed in the southeast coastal regions and “cold spots” being found in the central and western regions, so that as time goes on, the pattern of “high in the east and low in the west” has been gradually formed and stabilized. (4) The GWR model analysis showed that the natural factors such as NDVI, DMSP/OLS and DEM have a significant impact on land use efficiency, thereby providing an important contribution to this study. For the eastern coastal areas, the emphasis should be improving their OT, PF and PGDP, for the western region, should focus on improving its comprehensive economic development level to improve the DMSP/OLS, while strengthening the ecological environment to improve the level of NDVI.

Impaired Water Hazard Zones: Mapping Intersecting Environmental Health Vulnerabilities and Polluter Disproportionality

This study advanced a rigorous spatial analysis of surface water-related environmental health vulnerabilities in the California Bay-Delta region, USA, from 2000 to 2006. It constructed a novel hazard indicator—“impaired water hazard zones’’—from regulatory estimates of extensive non-point-source (NPS) and point-source surface water pollution, per section 303(d) of the U.S. Clean Water Act. Bivariate and global logistic regression (GLR) analyses examined how established predictors of surface water health-hazard exposure vulnerability explain census block groups’ proximity to impaired water hazard zones in the Bay-Delta. GLR results indicate the spatial concentration of Black disadvantage, isolated Latinx disadvantage, low median housing values, proximate industrial water pollution levels, and proximity to the Chevron oil refinery—a disproportionate, “super emitter”, in the Bay-Delta—significantly predicted block group proximity to impaired water hazard zones. A geographically weighted logistic regression (GWLR) specification improved model fit and uncovered spatial heterogeneity in the predictors of block group proximity to impaired water hazard zones. The modal GWLR results in Oakland, California, show how major polluters beyond the Chevron refinery impair the local environment, and how isolated Latinx disadvantage was the lone positively significant population vulnerability factor. The article concludes with a discussion of its scholarly and practical implications.

Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children's Lead Poisoning in Syracuse, NY, USA

Objective: The purpose of this study was to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods: Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children's lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children's blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. Results: At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children's lead poisoning. Further, these negative or positive relationships between children's lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). Conclusions: The GWQR models enabled us to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.

Geographical Factors Associated with Health Disparities in Prostate Cancer

Background

Treatment variation in prostate cancer is common, and it is driven by clinical and clinician factors, patient preferences, availability of resources, and access to physicians and treating facilities. Most research on treatment disparities in men with prostate cancer has focused on race and socioeconomic factors. However, the geography of disparities — capturing racial and socioeconomic differences based on where patients live — can provide insight into barriers to care and help identify outlier areas in which access to care, health resources, or both are more pronounced.

Methods

Research regarding treatment patterns and disparities in prostate cancer using the Geographical Information System (GIS) was searched. Studies were limited to English-language articles and research focused on US populations. A total of 43 articles were found; of those, 30 provided information about or used spatial or geographical analyses to assess and describe differences or disparities in prostate cancer and its treatment. Two additional GIS resources were included.

Results

The research on geographical and spatial determinants of prostate cancer disparities was reviewed. We also examined geographical analyses at the state level, focusing on Florida. Overall, we described a geographical framework to disparities that affect men with prostate cancer and reviewed existing published evidence supporting the interplay of geographical factors and disparities in prostate cancer.

Conclusions

Disparities in prostate cancer are common and persistent, and notable differences in treatment are observable across racial and socioeconomic strata. Geographical analysis provides additional information about where disparate groups live and also helps to map access to care. This information can be used by public health officials, health-systems administrators, clinicians, and policymakers to better understand and respond to geographical barriers that contribute to disparities in care.

Geospatial estimation of the impact of Deepwater Horizon oil spill on plant oiling along the Louisiana shorelines

Stranded oil covering soil and plant stems in fragile Louisiana marshes was one of the most visible impacts of the 2010 Deepwater Horizon (DWH) oil spill. As part of the assessment of marsh injury after the DWH spill, plant stem oiling was broken into five categories (0%, 0-10%, 10-50%, 50-90%, 90-100%) and used as the independent variable for estimating death of vegetation, accelerated erosion, and other metrics of injury. The length of shoreline falling into each of these stem oiling categories was therefore a key measure of the total extent of marsh injury, and its accurate estimation is the focus of this paper. First, we used geographically-weighted logistic regression (GWR) to explore and model spatially varying relationships between stem oiling field data and secondary information (oiling exposure category) collected during shoreline surveys. We then combined GWR probability estimates with field data using indicator cokriging to predict the probability of exceeding four stem oiling thresholds (0, 10, 50, and 90%) at 50 m intervals along the Louisiana shoreline. Cross-validation using Receiver Operating Characteristic (ROC) Curves demonstrate the greater prediction accuracy of the multivariate geostatistical approach relative to either aspatial regression or indicator kriging that ignores secondary information.

Spatial variations in the associations of term birth weight with ambient air pollution in Georgia, USA

Birth weight is an important indicator of overall infant health and a strong predictor of infant morbidity and mortality, and low birth weight (LBW) is a leading cause of infant mortality in the United States. Numerous studies have examined the associations of birth weight with ambient air pollution, but the results were inconsistent. In this study, a spatial statistical technique, geographically weighted regression (GWR) is applied to explore the spatial variations in the associations of birth weight with concentrations of ozone (O3) and fine particulate matter (PM2.5) in the State of Georgia, USA adjusted for gestational age, parity, and six other socioeconomic, behavioral, and land use factors. The results show considerable spatial variations in the associations of birth weight with both pollutants. Significant positive, non-significant, and significant negative relationships between birth weight and concentrations of each air pollutant are all found in different parts of the study area, and the different types of the relationships are affected by the socioeconomic and urban characteristics of the communities where the births are located. The significant negative relationships between birth weight and O3 indicate that O3 is a significant risk factor of LBW and these associations are primarily located in less-urbanized communities. On the other hand, PM2.5 is a significant risk factor of LBW in the more-urbanized communities with higher family income and education attainment. These findings suggest that environmental and health policies should be adjusted to address the different effects of air pollutants on birth outcomes across different types of communities to more effectively and efficiently improve birth outcomes.

Lung Cancer Mortality and Topography: A Xuanwei Case Study

The epidemic of lung cancer in Xuanwei City, China, remains serious despite the reduction of the risk of indoor air pollution through citywide stove improvement. The main objective of this study was to characterize the influences of topography on the spatiotemporal variations of lung cancer mortality in Xuanwei during 1990-2013. Using the spatially empirical Bayes method, the smoothed mortality rate of lung cancer was obtained according to the mortality data and population data collected from the retrospective survey (1990-2005) and online registration data (2011-2013). Spatial variations of the village-level mortality rate and topographic factors, including the relief degree of land surface (RDLS) and dwelling conditions (VDC), were characterized through spatial autocorrelation and hotspot analysis. The relationship between topographic factors and the epidemic of lung cancer was explored using correlation analysis and geographically weighted regression (GWR). There is a pocket-like area (PLA) in Xuanwei, covering the clustered villages with lower RDLS and higher VDC. Although the villages with higher mortality rate (>80 per 10⁵) geographically expanded from the center to the northeast of Xuanwei during 1990-2013, the village-level mortality rate was spatially clustered, which yielded a persistent hotspot area in the upward part of the PLA. In particular, the epidemic of lung cancer was closely correlated with both RDLS and VDC at the village scale, and its spatial heterogeneity could be greatly explained by the village-level VDC in the GWR model. Spatiotemporally featured lung cancer mortality in Xuanwei was potentially influenced by topographic conditions at the village scale.