Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark

Assessing and attenuating the impact of selection bias on spatial cluster detection studies

Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.

Constructing Residential Histories in a General Population-Based Representative Sample

Research on neighborhoods and health typically measures neighborhood context at a single point in time. However, neighborhood exposures accumulate over the life course, influenced by both residential mobility and neighborhood change, with potential implications for estimating the impact of neighborhoods on health. Commercial databases offer fine-grained longitudinal residential address data that can enrich life-course spatial epidemiology research, and validated methods for reconstructing residential histories from these databases are needed. Our study draws on unique data from a geographically diverse, population-based representative sample of adult Wisconsin residents and the LexisNexis (New York, New York) Accurint, a commercial personal profile database, to develop a systematic and reliable methodology for constructing individual residential histories. Our analysis demonstrated that creating residential histories across diverse geographical contexts is feasible, and it highlights differences in the information obtained from available residential histories by age, education, race/ethnicity, and rural/urban/suburban residency. Researchers should consider potential address data availability and information biases favoring socioeconomically advantaged individuals and their implications for studying health inequalities. Despite these limitations, LexisNexis data can generate varied residential exposure metrics and be linked to contextual data to enrich research into the contextual determinants of health at varied geographic scales.

Utilizing residential histories to assess environmental exposure and socioeconomic status over the life course among mesothelioma patients

Background

Exposure misclassification based solely on the address at cancer diagnosis has been widely recognized though not commonly assessed.

Methods

We linked 1,015 mesothelioma cases diagnosed during 2011-2015 from the New York State Cancer Registry to inpatient claims and LexisNexis administrative data and constructed residential histories. Percentile ranking of exposure to ambient air toxics and socioeconomic status (SES) were based on the National Air Toxic Assessment and United States Census data, respectively. To facilitate comparisons over time, relative exposures (REs) were calculated by dividing the percentile ranking at individual census tract by the state-level average and subtracting one. We used generalized linear regression models to compare the RE in the past with that at cancer diagnosis, adjusting for patient-level characteristics.

Results

Approximately 43.7% of patients had residential information available for up to 30 years, and 96.0% up to 5 years. The median number of unique places lived was 4 [interquartile range (IQR), 2-6]. The time-weighted-average RE from all addresses available had a median of -0.11 (IQR, -0.50 to 0.30) for air toxics and -0.28 (IQR, -0.65 to 0.25) for SES. RE associated with air toxics (but not SES) was significantly higher for earlier addresses than addresses at cancer diagnosis for the 5-year [annual increase =1.24%; 95% confidence interval (CI): 0.71-1.77%; n=974] and 30-year (annual increase =0.36%; 95% CI: 0.25-0.48%; n=444) look-back windows, respectively.

Conclusions

Environmental exposure to non-asbestos air toxics among mesothelioma patients may be underestimated if based solely on the address at diagnosis. With geospatial data becoming more readily available, incorporating cancer patients' residential history would lead to reduced exposure misclassification and accurate health risk estimates.

Challenges and opportunity in mobility among older adults - key determinant identification

BACKGROUND

Attention is focused on the health and physical fitness of older adults due to their increasing age. Maintaining physical abilities, including safe walking and movement, significantly contributes to the perception of health in old age. One of the early signs of declining fitness in older adults is limited mobility. Approximately one third of 70-year-olds and most 80-year-olds report restrictions on mobility in their apartments and immediate surroundings. Restriction or loss of mobility is a complex multifactorial process, which makes older adults prone to falls, injuries, and hospitalizations and worsens their quality of life while increasing overall mortality.

OBJECTIVE

The objective of the study is to identify the factors that have had a significant impact on mobility in recent years and currently, and to identify gaps in our understanding of these factors. The study aims to highlight areas where further research is needed and where new and effective solutions are required.

METHODS

The PRISMA methodology was used to conduct a scoping review in the Scopus and Web of Science databases. Papers published from 2007 to 2021 were searched in November 2021. Of these, 52 papers were selected from the initial 788 outputs for the final analysis.

RESULTS

The final selected papers were analyzed, and the key determinants were found to be environmental, physical, cognitive, and psychosocial, which confirms the findings of previous studies. One new determinant is technological. New and effective solutions lie in understanding the interactions between different determinants of mobility, addressing environmental factors, and exploring opportunities in the context of emerging technologies, such as the integration of smart home technologies, design of accessible and age-friendly public spaces, development of policies and regulations, and exploration of innovative financing models to support the integration of assistive technologies into the lives of seniors.

CONCLUSION

For an effective and comprehensive solution to support senior mobility, the determinants cannot be solved separately. Physical, cognitive, psychosocial, and technological determinants can often be perceived as the cause/motivation for mobility. Further research on these determinants can help to arrive at solutions for environmental determinants, which, in turn, will help improve mobility. Future studies should investigate financial aspects, especially since many technological solutions are expensive and not commonly available, which limits their use.

Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection

Background

Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants.

Objectives

(1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line.

Methods

Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing.

Results

Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86–2.96) during birth and childhood in the 1950’s–1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state.

Conclusion

This study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person’s geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00313-4.

Comparing spatial patterns of 11 common cancers in Mainland China

BACKGROUND

A stronger spatial clustering of cancer burden indicates stronger environmental and human behavioral effects. However, which common cancers in China have stronger spatial clustering and knowledge gaps regarding the environmental and human behavioral effects have yet to be investigated. This study aimed to compare the spatial clustering degree and hotspot patterns of 11 common cancers in mainland China and discuss the potential environmental and behavioral risks underlying the patterns.

METHODS

Cancer incidence data recorded at 339 registries in 2014 was obtained from the "China Cancer Registry Annual Report 2017". We calculated the spatial clustering degree of the common cancers using the global Moran's Index and identified the hotspot patterns using the hotspot analysis.

RESULTS

We found that esophagus, stomach and liver cancer have a significantly higher spatial clustering degree ([Formula: see text]) than others. When by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer had significantly higher spatial clustering degree ([Formula: see text]). The spatial clustering degree of male liver was significantly higher than that of female liver cancer ([Formula: see text]), whereas the spatial clustering degree of female lung was significantly higher than that of male lung cancer ([Formula: see text]). The high-risk areas of esophagus and stomach cancer were mainly in North China, Huai River Basin, Yangtze River Delta and Shaanxi Province. The hotspots for liver and male liver cancer were mainly in Southeast China and south Hunan. Hotspots of female lung cancer were mainly located in the Pearl River Delta, Shandong, North and Northeast China. The Yangtze River Delta and the Pearl River Delta were high-risk areas for multiple cancers.

CONCLUSIONS

The top highly clustered cancer types in mainland China included esophagus, stomach and liver cancer and, by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer. Among them, knowledge of their spatial patterns and environmental and behavioral risk factors is generally limited. Potential factors such as unhealthy diets, water pollution and climate factors have been suggested, and further investigation and validation are urgently needed, particularly for male liver cancer. This study identified the knowledge gap in understanding the spatial pattern of cancer burdens in China and offered insights into targeted cancer monitoring and control.

Ambient UVR and Environmental Arsenic Exposure in Relation to Cutaneous Melanoma in Iowa

Intermittent sun exposure is the major environmental risk factor for cutaneous melanoma (CM). Cumulative sun exposure and other environmental agents, such as environmental arsenic exposure, have not shown consistent associations. Ambient ultraviolet radiation (UVR) was used to measure individual total sun exposure as this is thought to be less prone to misclassification and recall bias. Data were analyzed from 1096 CM cases and 1033 controls in the Iowa Study of Skin Cancer and Its Causes, a population-based, case-control study. Self-reported residential histories were linked to satellite-derived ambient UVR, spatially derived environmental soil arsenic concentration, and drinking water arsenic concentrations. In men and women, ambient UVR during childhood and adolescence was not associated with CM but was positively associated during adulthood. Lifetime ambient UVR was positively associated with CM in men (OR for highest vs. lowest quartile: 6.09, 95% confidence interval (CI) 2.21–16.8), but this association was not as strong among women (OR for highest vs. lowest quartile: 2.15, 95% CI 0.84–5.54). No association was detected for environmental soil or drinking water arsenic concentrations and CM. Our findings suggest that lifetime and adulthood sun exposures may be important risk factors for CM.

Geographic clustering of cutaneous T-cell lymphoma in New Jersey: an exploratory analysis using residential histories

PURPOSE

Cutaneous T-cell lymphoma (CTCL) is a rare type of non-Hodgkin lymphoma. Previous studies have reported geographic clustering of CTCL based on the residence at the time of diagnosis. We explore geographic clustering of CTCL using both the residence at the time of diagnosis and past residences using data from the New Jersey State Cancer Registry.

METHODS

CTCL cases (n = 1,163) diagnosed between 2006-2014 were matched to colon cancer controls (n = 17,049) on sex, age, race/ethnicity, and birth year. Jacquez's Q-Statistic was used to identify temporal clustering of cases compared to controls. Geographic clustering was assessed using the Bernoulli-based scan-statistic to compare cases to controls, and the Poisson-based scan-statisic to compare the observed number of cases to the number expected based on the general population. Significant clusters (p < 0.05) were mapped, and standard incidence ratios (SIR) reported. We adjusted for diagnosis year, sex, and age.

RESULTS

The Q-statistic identified significant temporal clustering of cases based on past residences in the study area from 1992 to 2002. A cluster was detected in 1992 in Bergen County in northern New Jersey based on the Bernoulli (1992 SIR 1.84) and Poisson (1992 SIR 1.86) scan-statistics. Using the Poisson scan-statistic with the diagnosis location, we found evidence of an elevated risk in this same area, but the results were not statistically significant.

CONCLUSION

There is evidence of geographic clustering of CTCL cases in New Jersey based on past residences. Additional studies are necessary to understand the possible reasons for the excess of CTCL cases living in this specific area some 8-14 years prior to diagnosis.

Residential Mobility and Geospatial Disparities in Colon Cancer Survival

BACKGROUND

Identifying geospatial cancer survival disparities is critical to focus interventions and prioritize efforts with limited resources. Incorporating residential mobility into spatial models may result in different geographic patterns of survival compared with the standard approach using a single location based on the patient's residence at the time of diagnosis.

METHODS

Data on 3,949 regional-stage colon cancer cases diagnosed from 2006 to 2011 and followed until December 31, 2016, were obtained from the New Jersey State Cancer Registry. Geographic disparity based on the spatial variance and effect sizes from a Bayesian spatial model using residence at diagnosis was compared with a time-varying spatial model using residential histories [adjusted for sex, gender, substage, race/ethnicity, and census tract (CT) poverty]. Geographic estimates of risk of colon cancer death were mapped.

RESULTS

Most patients (65%) remained at the same residence, 22% changed CT, and 12% moved out of state. The time-varying model produced a wider range of adjusted risk of colon cancer death (0.85-1.20 vs. 0.94-1.11) and resulted in greater geographic disparity statewide after adjustment (25.5% vs. 14.2%) compared with the model with only the residence at diagnosis.

CONCLUSIONS

Including residential mobility may allow for more precise estimates of spatial risk of death. Results based on the traditional approach using only residence at diagnosis were not substantially different for regional stage colon cancer in New Jersey.

IMPACT

Including residential histories opens up new avenues of inquiry to better understand the complex relationships between people and places, and the effect of residential mobility on cancer outcomes.See related commentary by Williams, p. 2107.