Deriving life-course residential histories in brain bank cohorts: A feasibility study

INTRODUCTION

The exposome is theorized to interact with biological mechanisms to influence risk for Alzheimer's disease but is not well-integrated into existing Alzheimer's Disease Research Center (ADRC) brain bank data collection.

METHODS

We apply public data tracing, an iterative, dual abstraction and validation process rooted in rigorous historic archival methods, to develop life-course residential histories for 1254 ADRC decedents.

RESULTS

The median percentage of the life course with an address is 78.1% (IQR 24.9); 56.5% of the sample has an address for at least 75% of their life course. Archivists had 89.7% agreement at the address level. This method matched current residential survey methodology 97.4% on average.

DISCUSSION

This novel method demonstrates feasibility, reproducibility, and rigor for historic data collection. To our knowledge, this is the first study to show that public data tracing methods for brain bank decedent residential history development can be used to better integrate the social exposome with biobank specimens.

HIGHLIGHTS

Public data tracing compares favorably to survey-based residential history. Public data tracing is feasible and reproducible between archivists. Archivists achieved 89.7% agreement at the address level. This method identifies residences for nearly 80% of life-years, on average. This novel method enables brain banks to add social characterizations.

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.

Association of mesothelioma deaths with the neighborhood asbestos exposure due to a large-scale asbestos-cement plant

A causal relationship between mesothelioma and the occupational asbestos exposure is well known, while few studies have shown a relationship to non-occupational exposures. The aim of this study was to quantify the risk of mesothelioma death associated with neighborhood asbestos exposures due to a large-scale asbestos-cement (AC) plant in Amagasaki, Japan, adjusting properly considerable risk factors including occupational exposures. We conducted a nested case-control study in which a fixed population of 143,929 residents who had been living in Amagasaki City between 1975 and 2002 were followed from 2002 to 2015. All 133 cases and 403 matched controls were interviewed about their occupational, domestic, household, and neighborhood asbestos exposures. Odds ratios (ORs) for mesothelioma death associated with the neighborhood exposure were estimated by a conditional logistic-regression model. For quantitative assessments for neighborhood exposure, we adopted cumulative indices for individuals' residential histories at each residence-specific asbestos concentration multiplied by the duration during the potential exposure period of 1957-1975 (crocidolite). We observed an increasing, dose-dependent risk of mesothelioma death associated with the neighborhood exposure, demonstrating that ORs in the highest quintile category were 21.4 (95% CI: 5.8 - 79.2) for all, 23.7 (95% CI: 3.8 -147.2) for males, and 26.0 (95% CI: 2.8 - 237.5) for females compared to the lowest quintile, respectively. A quantitative assessment for risk of mesothelioma deaths, adjusting for occupational and the other non-occupational exposures separately, showed a dose-dependent association with the neighborhood exposure and no substantial gender differences in magnitude.

Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low-rank kriging multiple membership model

The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low-rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK-MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case-control study of non-Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK-MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

Estimating cumulative spatial risk over time with low-rank kriging multiple membership models

Many health outcomes result from accumulated exposures to one or more environmental factors. Accordingly, spatial risk studies have begun to consider multiple residential locations of participants, acknowledging that participants move and thus are exposed to environmental factors in several places. However, novel methods are needed to estimate cumulative spatial risk for disease while accounting for other risk factors. To this end, we propose a Bayesian model (LRK-MMM) that embeds a multiple membership model (MMM) into a low-rank kriging (LRK) model in order to estimate cumulative spatial risk at the point level while allowing for multiple residential locations per subject. The LRK approach offers a more computationally efficient means to analyze spatial risk in case-control study data at the point level compared with a Bayesian generalized additive model, and as increased precision in spatial risk estimates by analyzing point locations instead of administrative areas. Through a simulation study, we demonstrate the efficacy of the model and its improvement upon an existing multiple membership model that uses area-level spatial random effects to estimate risk. The results show that our proposed method provides greater spatial sensitivity (improvements ranging from 0.12 to 0.54) and power (improvements ranging from 0.02 to 0.94) to detect regions of elevated risk for disease across a range of exposure scenarios. Finally, we apply our model to case-control data from the New England bladder cancer study to estimate cumulative spatial risk while adjusting for many covariates.

Kidney Cancer Risk Associated with Historic Groundwater Trichloroethylene Contamination

Trichloroethylene (TCE) is a well-documented kidney carcinogen based on a substantial body of evidence including mechanistic and animal studies, as well as reports from occupational settings. However, the cancer risks for those in residential exposures such as TCE contamination in groundwater are much less clear. The objective of this study was to perform a detailed spatio-temporal analysis of estimated residential TCE exposure in New Hampshire, US. We identified kidney cancer cases (n = 292) and age-, gender-matched controls (n = 448) from the Dartmouth-Hitchcock Health System and queried a commercial financial database for address histories. We used publically available data on TCE levels in groundwater measured at contaminated sites in New Hampshire and then modeled the spatial dispersion and temporal decay. We overlaid geospatial residential locations of cases and controls with yearly maps of estimated TCE levels to estimate median exposures over the 5, 10, and 15-year epochs before diagnosis. The 50th-75th percentile of estimated residential exposure over a 15-year period was associated with increased kidney cancer risk (adjusted Odds Ratio (OR) 1.78 95% CI 1.05-3.03), compared to <50th percentile. This finding supports the need for groundwater monitoring of TCE contaminated sites to identify potential public health risks.

Assessment of Interstate Residential Mobility of SEER Patients: SEER and LexisNexis Residential Address Linkage

The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) program is continuously exploring opportunities to augment its already extensive collection of data, enhance the quality of reported cancer information, and contribute to more comprehensive analyses of cancer burden. This manuscript describes a recent linkage of the LexisNexis longitudinal residential history data with 11 SEER registries and provides estimates of the inter-state mobility of SEER cancer patients. To identify mobility from one state to another, we used state postal abbreviations to generate state-level residential histories. From this, we determined how often cancer patients moved from state-to-state. The results in this paper provide information on the linkage with LexisNexis data and useful information on state-to-state residential mobility patterns of a large portion of US cancer patients for the most recent 1-, 2-, 3-, 4-, and 5-year periods. We show that mobility patterns vary by geographic area, race/ethnicity and age, and cancer patients tend to move less than the general population.

On the impact of residential history in the spatial analysis of diseases with a long latency period: A study of mesothelioma in Belgium

Mesothelioma is a rare cancer caused by exposure to asbestos. Belgium has a known long history of asbestos production, resulting in one of the highest mesothelioma mortality rates worldwide. While the production of asbestos has stopped completely, the long latency period of mesothelioma, which can fluctuate between 20 and 40 years after exposure, causes incidences still to be frequent. Mesothelioma's long incubation time affects our assessment of its geographical distribution as well. Since patients' residential locations are likely to change a number of times throughout their lives, the location where the patients develop the disease is often far from the location where they were exposed to asbestos. Using the residential history of patients, we propose the use of a convolution multiple membership model (MMM), which includes both a spatial conditional autoregressive and an unstructured random effect. Pancreatic cancer patients are used as a control population, reflecting the population at risk for mesothelioma. Results show the impact of the residential mobility on the geographical risk estimation, as well as the importance of acknowledging the latency period of a disease. A simulation study was conducted to investigate the properties of the convolution MMM. The robustness of the results for the convolution MMM is assessed via a sensitivity analysis.

Moderating Effect of Residential History on the Effects of a Fatherhood Program on Parenting Skills Satisfaction among Nonresident African American Fathers

Nonresident African American (AA) fathers sometimes face challenges to achieving satisfaction with their parenting skills, which may inhibit their motivations for parenting. Studies have found that residential history of fathers is associated with parental involvement; however, current fatherhood programs rarely consider the influence of different residential history on fathering. In the current study, we examined whether nonresident AA fathers’ residential history with their sons moderated their parenting skills satisfaction after participating in the Fathers and Sons Program. Our results indicated that after controlling for fathers’ pretest parenting skills satisfaction, age, education, marital status, employment, and ever lived with their son’s mother; there was a moderating effect of residential history on the intervention’s effects on posttest parenting skills satisfaction. The regression analyses showed that fathers in the intervention group who had lived with their son increased their parenting skills satisfaction more at posttest compared with fathers who had never lived with their sons. However, fathers in the comparison group who had lived with their sons had lower posttest parenting skills satisfaction. Future fatherhood programs for nonresident AA fathers should develop more nuanced group-specific interventions that consider residential history as a critical factor to enhance their parenting skills satisfaction as a strategy for improving father involvement.

Longitudinal designs to study neighbourhood effects on the development of obesity: a scoping review protocol

INTRODUCTION

The prevalence of obesity has increased significantly in the last three decades and became an important public health concern. Evidence of weight status variability at the neighbourhood level has led researchers to look more precisely at the characteristics of local geographic areas that might influence energy balance related behaviours, giving rise to the field of the 'neighbourhood effect' in public health research. Among an abundant literature about neighbourhood effects and obesity, we propose a protocol for a scoping review that will aim at determining how temporal measurements of residential neighbourhood exposure, individual covariates and weight outcome are integrated in longitudinal designs.

METHODS AND ANALYSIS

A list of relevant citations will be obtained through a comprehensive systematic database search in Pubmed, Web of Science and Embase. The search strategy will be designed using a broad definition of neighbourhood to take into account the heterogeneity of this concept in research. Two investigators will screen titles, abstracts and entire publications using predetermined eligibility criteria yielding a list of selected publications. Data from the publications included in the scoping review will be charted according to bibliographic information, study population, exposure, outcomes and results.

DISCUSSION AND CONCLUSION

To our knowledge, our protocol will yield the first scoping review regarding longitudinal designs of neighbourhood effect on obesity. Describing how longitudinal designs include temporal measurements of exposure, covariates and outcome is a necessary step in the quest to determine if or which contextual characteristics are likely to be involved in the development of obesity. Such information would bring new knowledge to complement current aetiological investigations and would contribute to enhancing resource allocation strategies for stakeholders in developing relevant interventions to prevent obesity and its negative impacts.

Tracing a Path to the Past: Exploring the Use of Commercial Credit Reporting Data to Construct Residential Histories for Epidemiologic Studies of Environmental Exposures

Large-scale environmental epidemiologic studies often rely on exposure estimates based on linkage to residential addresses. This approach, however, is limited by the lack of residential histories typically available for study participants. Our objective was to evaluate the feasibility of using address data from LexisNexis (a division of RELX, Inc., Dayton, Ohio), a commercially available credit reporting company, to construct residential histories for participants in the California Teachers Study (CTS), a prospective cohort study initiated in 1995–1996 to study breast cancer (n = 133,479). We evaluated the degree to which LexisNexis could provide retrospective addresses prior to study enrollment, as well as the concordance with existing prospective CTS addresses ascertained at the time of the completion of 4 self-administered questionnaires. For approximately 80% of CTS participants, LexisNexis provided at least 1 retrospective address, including nearly 25,000 addresses completely encompassed by time periods prior to enrollment. This approach more than doubled the proportion of the study population for whom we had an address of residence during the childbearing years—an important window of susceptibility for breast cancer risk. While overall concordance between the prospective addresses contained in these 2 data sources was good (85%), it was diminished among black women and women under the age of 40 years.

Assessment of Residential History Generation Using a Public-Record Database

In studies of disease with potential environmental risk factors, residential location is often used as a surrogate for unknown environmental exposures or as a basis for assigning environmental exposures. These studies most typically use the residential location at the time of diagnosis due to ease of collection. However, previous residential locations may be more useful for risk analysis because of population mobility and disease latency. When residential histories have not been collected in a study, it may be possible to generate them through public-record databases. In this study, we evaluated the ability of a public-records database from LexisNexis to provide residential histories for subjects in a geographically diverse cohort study. We calculated 11 performance metrics comparing study-collected addresses and two address retrieval services from LexisNexis. We found 77% and 90% match rates for city and state and 72% and 87% detailed address match rates with the basic and enhanced services, respectively. The enhanced LexisNexis service covered 86% of the time at residential addresses recorded in the study. The mean match rate for detailed address matches varied spatially over states. The results suggest that public record databases can be useful for reconstructing residential histories for subjects in epidemiologic studies.