Constructing regions for small area analysis: material deprivation and colorectal cancer

Regionalization and Partitioning of Soil Health Indicators for Nigeria Using Spatially Contiguous Clustering for Economic and Social-Cultural Developments

Understanding the spatial variability of soil health and identifying areas that share similar soil properties can help nations transition to sustainable agricultural practices. This information is particularly applicable to management decisions such as tillage, nutrient application, and soil and water conservation. This study evaluated the spatial variability and derived the optimal number of spatially contiguous regions of Nigeria’s 774 Local Government Areas (LGAs) using three soil health indicators, organic carbon (OC), bulk density (BD) and total nitrogen (TN) extracted from the Africa Soil Information Service database. Missing data were imputed using the random forest imputation method with topography and normalized difference vegetation index (NDVI) as auxiliary variables. Using an exponential covariance function, the spatial ranges for BD, SN, and OC were calculated as 18, 42, and 60 km, respectively. These were the maximum distances at which there was no correlation between the sample data points. This finding suggests that OC has high variability across Nigeria as compared with other tested indicators. The ordinary kriging (OK) technique revealed spatial dependency (positive correlation) among TN and OC on interpolated surfaces, with high values in the southern part of the county and low values in the north. The BD values were also high in the northern regions where the soils are sandy; correspondingly, TN and OC had low values. The “regionalization with dynamically constrained agglomerative clustering and partitioning” (REDCAP) technique was used to divide LGAs into a possible number of regions while optimizing a sum of squares deviation (SSD). Optimal division was not observed in the resulting number of regional partitions. Conducting the Markov Chain Monte Carlo (MCMC) method on within-zone heterogeneity (WZH) revealed three partitions (two, five, and 15 regions) as optimal, in other words, there would be no significant change in WZH after three partitions. Ensuring a proper understanding of soil spatial variability and heterogeneities (or homogeneities) could facilitate agricultural planning that combines or merges state and local governments that share the same soil health properties, rather than basing decisions on geopolitical, racial, or ethnoreligious factors. The findings of this study could be applied to understand the importance of soil heterogeneities in hydrologic modeling applications. In addition, the findings may aid decision-making bodies such as the United Nations’ Food and Agricultural Organization, the International Fund for Agricultural Development, or the World Bank in their efforts to alleviate poverty, meet future food needs, mitigate the impacts of climate change, and provide financial funding through sustainable agriculture and intervention in developing countries such as Nigeria.

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S

This study identified geographic disparities in breast cancer mortality across the U.S. using kriging to overcome unavailability of data because of confidentiality and reliability concerns. A structured additive regression model was used to detect where breast cancer mortality rates were elevated across nine divisions with 3109 U.S. counties during 1982-2004. Our analysis identified at least 25.8% of counties where breast cancer mortality rates were elevated. High-risk counties compared to lower-risk counties had higher relative risks for African American women than for White women. Greater geographic disparities more likely present in African American women and younger women. To sum up, our statistical approach reduced the impact of unavailable data, and identified the number and location of counties with high breast cancer mortality risk by race and age across the U.S.

Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review

Despite spending more than any other nation on medical care per person, the United States ranks behind other industrialized nations in key health performance measures. A main cause is the deep disparities in access to care and health outcomes. Federal programs such as the designations of Medically Underserved Areas/Populations and Health Professional Shortage Areas are designed to boost the number of health professionals serving these areas and to help alleviate the access problem. Their effectiveness relies first and foremost on an accurate measure of accessibility so that resources can be allocated to truly needy areas. Various measures of accessibility need to be integrated into one framework for comparison and evaluation. Optimization methods can be used to improve the distribution and supply of health care providers to maximize service coverage, minimize travel needs of patients, limit the number of facilities, and maximize health or access equality. Inequality in health care access comes at a personal and societal price, evidenced in disparities in health outcomes, including late-stage cancer diagnosis. This review surveys recent literature on the three named issues with emphasis on methodological advancements and implications for public policy.

Spatiotemporal analysis and mapping of oral cancer risk in changhua county (taiwan): an application of generalized bayesian maximum entropy method

PURPOSE

Incidence rate of oral cancer in Changhua County is the highest among the 23 counties of Taiwan during 2001. However, in health data analysis, crude or adjusted incidence rates of a rare event (e.g., cancer) for small populations often exhibit high variances and are, thus, less reliable.

METHODS

We proposed a generalized Bayesian Maximum Entropy (GBME) analysis of spatiotemporal disease mapping under conditions of considerable data uncertainty. GBME was used to study the oral cancer population incidence in Changhua County (Taiwan). Methodologically, GBME is based on an epistematics principles framework and generates spatiotemporal estimates of oral cancer incidence rates. In a way, it accounts for the multi-sourced uncertainty of rates, including small population effects, and the composite space-time dependence of rare events in terms of an extended Poisson-based semivariogram.

RESULTS

The results showed that GBME analysis alleviates the noises of oral cancer data from population size effect. Comparing to the raw incidence data, the maps of GBME-estimated results can identify high risk oral cancer regions in Changhua County, where the prevalence of betel quid chewing and cigarette smoking is relatively higher than the rest of the areas.

CONCLUSIONS

GBME method is a valuable tool for spatiotemporal disease mapping under conditions of uncertainty.

Flow mapping and multivariate visualization of large spatial interaction data

Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.

The correlation between indices of deprivation and health-related quality of life in patients with oral and oropharyngeal squamous cell carcinoma

Deprivation influences the incidence and outcome of patients with cancer. Health-related quality of life (HR-QoL) is an important measure of outcome but there is little on this subject and its correlation with deprivation in patients with oral and oropharyngeal cancer. Our aim was to ascertain the extent of deprivation in a cohort of patients who had operations for primary oral and oropharyngeal cancer and how deprivation affected HR-QoL in these patients during and after their treatment. A total of 278 consecutive patients who were operated on for oral and oropharyngeal cancer were included in this study. Validated measures of deprivation scores (Townsend, Carstairs, Jarman and Index of Multiple Deprivation 2000) were calculated, as well as patient-based indicators of social circumstances (marital status, smoking habit, alcohol intake). Comparison was made with the University of Washington-Quality of Life (UW-QoL) scores before and after treatment (6, 12, and 24 months).

Geocoding in cancer research: a review

There is now widespread agreement that geographic identifiers (geocodes) should be assigned to cancer records, but little agreement on their form and how they should be assigned, reported, and used. This paper reviews geocoding practice in relation to major purposes and discusses methods to improve the accuracy of geocoded cancer data. Differences in geocoding methods and materials introduce errors of commission and omission into geocoded data. A common source of error comes from the practice of using digital boundary files of dubious quality to place addresses into areas of interest. Geocoded data are linked to demographic, environmental, and health services data, and each data type has unique accuracy considerations. In health services applications, the accuracy of distances computed from geocodes can differ markedly. Privacy and confidentiality issues are important in the use and release of geocoded cancer data. When masking methods are used for disclosure limitation purposes, statistical methods must be adjusted for the locational uncertainty of geocoded data. We conclude that selection of one particular type of geographic area as the geocode may unnecessarily constrain future work. Therefore, the longitude and latitude of each case is the superior basic geocode; all other geocodes of interest can be constructed from this basic identifier.

Spatial clusters of cancers in Illinois 1986-2000

Elevated cancer rates in some areas may arise simply by chance. The pattern generally warrants a study only when it is statistically significant. This research uses a recently developed spatial statistic, implemented in a Geographic Information System (GIS) environment, to detect spatial clusters of diagnostically specific cancers in Illinois. On the basis of the cancer incidence data (the 1986-1990 and 1996-2000 data sets) from the Illinois Cancer Registry, the study examines different clustering patterns of four leading types of cancer in Illinois, namely breast, lung, colorectal, and prostate cancers. The first part of the study uses the data at the county level, and the second part uses the data in zip code areas. The analysis using the zip code area data directly may be problematic since the rate estimates for rare events like cancer in small populations are susceptible to data errors. The spatial order method is used to group zip code areas so that the new geographic areas have sufficiently large base populations for estimates of reliable cancer rates. Results from the spatial cluster analysis may be valuable for other researchers to design follow-up case-control and retrospective cohort studies.

Colorectal cancer, screening and survival: the influence of socio-economic deprivation

OBJECTIVES

To determine the extent to which socio-economic deprivation explains colorectal cancer prevalence, subject participation in screening, and postoperative survival and life expectancy.

METHODS

Regression analyses of clinical data from a large randomized controlled trial, augmented by geographical-based indices of deprivation.

RESULTS

Deprivation appears to exert no significant impact on colorectal cancer prevalence but is a major factor explaining subject participation in screening. Cancer detection at later stages reduces life expectancy at time of treatment. Females from more-deprived areas have poorer post-treatment life expectancies and survival prospects, independently of their screening behaviour.

CONCLUSIONS

Screening increases the chances of having a cancer treated at an earlier stage, and treatment at an earlier stage is associated with longer subsequent life expectancy. However, those from more-deprived areas are less likely to accept an invitation to be screened.

Tuberculosis, deprivation, and ethnicity in Leeds, UK, 1982-1997

AIMS

To determine whether tuberculosis is increasing in frequency and to explore the association between deprivation, ethnicity, and tuberculosis in the city of Leeds.

METHODS

Descriptive epidemiology and ecological analysis of a register of children and young people (0-18 years) diagnosed with tuberculosis from 1982 to 1997 in Leeds Health Authority.

RESULTS

A total of 107 children were identified, 61 through contact tracing, to give an age and sex standardised incidence rate of 3.9 per 100 000 per year. Rates decreased over the 16 year study period by an estimated 6.6% per year. The disease was more common in girls (56%) and most frequent in 5-9 year olds, with respiratory disease accounting for the largest proportion (82%). Children of south Asian origin (35%) had a crude incidence rate of 25.7 per 100 000 per year. The female:male ratios differed notably between south Asian (1.9:1) and non-south Asian children (1.02:1). For all subjects, univariate analyses showed significant positive associations between incidence and deprivation, population density, and ethnicity. There were no significant associations between deprivation, population density, and ethnicity and incidence of tuberculosis in south Asian children. For non-south Asian, mainly white children, only deprivation was significant. The proportion of non-south Asian children in the population was the overriding factor influencing incidence of tuberculosis.

CONCLUSIONS

Tuberculosis remains an uncommon disease in Leeds children. An unexpected finding was a relatively higher incidence in Asian girls compared to boys. Overall, ethnicity explains a high proportion of disease independently of deprivation and population density but for non-south Asian Leeds children the strongest risk factor is deprivation.