Spatial-temporal analysis of breast cancer in upper Cape Cod, Massachusetts

INTRODUCTION. The reasons for elevated breast cancer rates in the upper Cape Cod area of Massachusetts remain unknown despite several epidemiological studies that investigated possible environmental risk factors. Data from two of these population-based case-control studies provide geocoded residential histories and information on confounders, creating an invaluable dataset for spatial-temporal analysis of participants' residency over five decades. METHODS. The combination of statistical modeling and mapping is a powerful tool for visualizing disease risk in a spatial-temporal analysis. Advances in geographic information systems (GIS) enable spatial analytic techniques in public health studies previously not feasible. Generalized additive models (GAMs) are an effective approach for modeling spatial and temporal distributions of data, combining a number of desirable features including smoothing of geographical location, residency duration, or calendar years; the ability to estimate odds ratios (ORs) while adjusting for confounders; selection of optimum degree of smoothing (span size); hypothesis testing; and use of standard software. We conducted a spatial-temporal analysis of breast cancer case-control data using GAMs and GIS to determine the association between participants' residential history during 1947–1993 and the risk of breast cancer diagnosis during 1983–1993. We considered geographic location alone in a two-dimensional space-only analysis. Calendar year, represented by the earliest year a participant lived in the study area, and residency duration in the study area were modeled individually in one-dimensional time-only analyses, and together in a two-dimensional time-only analysis. We also analyzed space and time together by applying a two-dimensional GAM for location to datasets of overlapping calendar years. The resulting series of maps created a movie which allowed us to visualize changes in magnitude, geographic size, and location of elevated breast cancer risk for the 40 years of residential history that was smoothed over space and time. RESULTS. The space-only analysis showed statistically significant increased areas of breast cancer risk in the northern part of upper Cape Cod and decreased areas of breast cancer risk in the southern part (p-value = 0.04; ORs: 0.90–1.40). There was also a significant association between breast cancer risk and calendar year (p-value = 0.05; ORs: 0.53–1.38), with earlier calendar years resulting in higher risk. The results of the one-dimensional analysis of residency duration and the two-dimensional analysis of calendar year and duration showed that the risk of breast cancer increased with increasing residency duration, but results were not statistically significant. When we considered space and time together, the maps showed a large area of statistically significant elevated risk for breast cancer near the Massachusetts Military Reservation (p-value range:0.02–0.05; ORs range: 0.25–2.5). This increased risk began with residences in the late 1940s and remained consistent in size and location through the late 1950s. CONCLUSION. Spatial-temporal analysis of the breast cancer data may help identify new exposure hypotheses that warrant future epidemiologic investigations with detailed exposure models. Our methods allow us to visualize breast cancer risk, adjust for known confounders including age at diagnosis or index year, family history of breast cancer, parity and age at first live- or stillbirth, and test for the statistical significance of location and time. Despite the advantages of GAMs, analyses are for exploratory purposes and there are still methodological issues that warrant further research. This paper illustrates that GAM methods are a suitable alternative to widely-used cluster detection methods and may be preferable when residential histories from existing epidemiological studies are available.National Cancer Institute (5R03CA119703-02); National Institute of Enviornmental Health (5P42ES007381

Method for mapping population-based case-control studies: an application using generalized additive models

BACKGROUND: Mapping spatial distributions of disease occurrence and risk can serve as a useful tool for identifying exposures of public health concern. Disease registry data are often mapped by town or county of diagnosis and contain limited data on covariates. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can provide detailed information on residential history and covariates. RESULTS: Generalized additive models (GAMs) provide a useful framework for mapping point-based epidemiologic data. Smoothing on location while controlling for covariates produces adjusted maps. We generate maps of odds ratios using the entire study area as a reference. We smooth using a locally weighted regression smoother (loess), a method that combines the advantages of nearest neighbor and kernel methods. We choose an optimal degree of smoothing by minimizing Akaike's Information Criterion. We use a deviance-based test to assess the overall importance of location in the model and pointwise permutation tests to locate regions of significantly increased or decreased risk. The method is illustrated with synthetic data and data from a population-based case-control study, using S-Plus and ArcView software. CONCLUSION: Our goal is to develop practical methods for mapping population-based case-control and cohort studies. The method described here performs well for our synthetic data, reproducing important features of the data and adequately controlling the covariate. When applied to the population-based case-control data set, the method suggests spatial confounding and identifies statistically significant areas of increased and decreased odds ratios

Common Statistical Pitfalls in Basic Science Research

In this review, we focused on common sources of confusion and errors in the analysis and interpretation of basic science studies. The issues addressed are seen repeatedly in the authors\u27 editorial experience, and we hope this article will serve as a guide for those who may submit their basic science studies to journals that publish both clinical and basic science research. We have discussed issues related to sample size and power, study design, data analysis, and presentation of results. We then illustrated these issues using a set of examples from basic science research studies

Renal Hyperfiltration and the Development of Microalbuminuria in Type 1 Diabetes

OBJECTIVE: The purpose of this study was to examine prospectively whether renal hyperfiltration is associated with the development of microalbuminuria in patients with type 1 diabetes, after taking into account known risk factors. RESEARCH DESIGN AND METHODS: The study group comprised 426 participants with normoalbuminuria from the First Joslin Kidney Study, followed for 15 years. Glomerular filtration rate was estimated by serum cystatin C, and hyperfiltration was defined as exceeding the 97.5th percentile of the sex-specific distribution of a similarly aged, nondiabetic population (134 and 149 ml/min per 1.73 m2 for men and women, respectively). The outcome was time to microalbuminuria development (multiple albumin excretion rate >30 μg/min). Hazard ratios (HRs) for microalbuminuria were calculated at 5, 10, and 15 years. RESULTS: Renal hyperfiltration was present in 24% of the study group and did not increase the risk of developing microalbuminuria. The unadjusted HR for microalbuminuria comparing those with and without hyperfiltration at baseline was 0.8 (95% CI 0.4–1.7) during the first 5 years, 1.0 (0.6–1.7) during the first 10 years, and 0.8 (0.5–1.4) during 15 years of follow-up. The model adjusted for baseline known risk factors including A1C, age at diagnosis of diabetes, diabetes duration, and cigarette smoking resulted in similar HRs. In addition, incorporating changes in hyperfiltration status during follow-up had minimal impact on the HRs for microalbuminuria. CONCLUSION;S Renal hyperfiltration does not have an impact on the development of microalbuminuria in type 1 diabetes during 5, 10, or 15 years of follow-up.National Institutes of Health Grant (DK 041526

Prenatal Exposure to Tetrachloroethylene-Contaminated Drinking Water and the Risk of Congenital Anomalies: A Retrospective Cohort Study

BACKGROUND: Prior animal and human studies of prenatal exposure to solvents including tetrachloroethylene (PCE) have shown increases in the risk of certain congenital anomalies among exposed offspring. OBJECTIVES: This retrospective cohort study examined whether PCE contamination of public drinking water supplies in Massachusetts influenced the occurrence of congenital anomalies among children whose mothers were exposed around the time of conception. METHODS: The study included 1,658 children whose mothers were exposed to PCE-contaminated drinking water and a comparable group of 2,999 children of unexposed mothers. Mothers completed a self-administered questionnaire to gather information on all of their prior births, including the presence of anomalies, residential histories and confounding variables. PCE exposure was estimated using EPANET water distribution system modeling software that incorporated a fate and transport model. RESULTS: Children whose mothers had high exposure levels around the time of conception had an increased risk of congenital anomalies. The adjusted odds ratio of all anomalies combined among children with prenatal exposure in the uppermost quartile was 1.5 (95% CI: 0.9, 2.5). No meaningful increases in the risk were seen for lower exposure levels. Increases were also observed in the risk of neural tube defects (OR: 3.5, 95% CI: 0.8, 14.0) and oral clefts (OR 3.2, 95% CI: 0.7, 15.0) among offspring with any prenatal exposure. CONCLUSION: The results of this study suggest that the risk of certain congenital anomalies is increased among the offspring of women who were exposed to PCE-contaminated drinking water around the time of conception. Because these results are limited by the small number of children with congenital anomalies that were based on maternal reports, a follow-up investigation should be conducted with a larger number of affected children who are identified by independent records.National Institute of Environmental Health (5 P42 ES007381); National Institutes of Healt

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

<p>Abstract</p> <p>Background</p> <p>A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.</p> <p>Results</p> <p>This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.</p> <p>Conclusions</p> <p>The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.</p

A multilevel non-hierarchical study of birth weight and socioeconomic status

<p>Abstract</p> <p>Background</p> <p>It is unclear whether the socioeconomic status (SES) of the community of residence has a substantial association with infant birth weight. We used multilevel models to examine associations of birth weight with family- and community-level SES in the Cape Cod Family Health Study. Data were collected retrospectively on births to women between 1969 and 1983 living on Cape Cod, Massachusetts. The sample included siblings born in different residences with differing community-level SES.</p> <p>Methods</p> <p>We used cross-classified models to account for multiple levels of correlation in a non-hierarchical data structure. We accounted for clustering at family- and community-levels. Models included extensive individual- and family-level covariates. SES variables of interest were maternal education; paternal occupation; percent adults living in poverty; percent adults with a four year college degree; community mean family income; and percent adult unemployment.</p> <p>Results</p> <p>Residual correlation was detected at the family- but not the community-level. Substantial effects sizes were observed for family-level SES while smaller magnitudes were observed for community-level SES. Overall, higher SES corresponded to increased birth weight though neither family- nor community-level variables had significant associations with the outcome. In a model applied to a reduced sample that included a single child per family, enforcing a hierarchical data structure, paternal occupation was found to have a significant association with birth weight (p = 0.033). Larger effect sizes for community SES appeared in models applied to the full sample that contained limited covariates, such as those typically found on birth certificates.</p> <p>Conclusions</p> <p>Cross-classified models allowed us to include more than one child per family even when families moved between births. There was evidence of mild associations between family SES and birth weight. Stronger associations between paternal occupation and birth weight were observed in models applied to reduced samples with hierarchical data structures, illustrating consequences of excluding observations from the cross-classified analysis. Models with limited covariates showed associations of birth weight with community SES. In models adjusting for a complete set of individual- and family-level covariates, community SES was not as important.</p

Exposure to Polyfluoroalkyl Chemicals and Attention Deficit/Hyperactivity Disorder in U.S. Children 12–15 Years of Age

Background: Polyfluoroalkyl chemicals (PFCs) have been widely used in consumer products. Exposures in the United States and in world populations are widespread. PFC exposures have been linked to various health impacts, and data in animals suggest that PFCs may be potential developmental neurotoxicants. Objectives: We evaluated the associations between exposures to four PFCs and parental report of diagnosis of attention deficit/hyperactivity disorder (ADHD). Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2000 and 2003–2004 for children 12–15 years of age. Parental report of a previous diagnosis by a doctor or health care professional of ADHD in the child was the primary outcome measure. Perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorohexane sulfonic acid (PFHxS) levels were measured in serum samples from each child. Results: Parents reported that 48 of 571 children included in the analysis had been diagnosed with ADHD. The adjusted odds ratio (OR) for parentally reported ADHD in association with a 1-μg/L increase in serum PFOS (modeled as a continuous predictor) was 1.03 [95% confidence interval (CI), 1.01–1.05]. Adjusted ORs for 1-μg/L increases in PFOA and PFHxS were also statistically significant (PFOA: OR = 1.12; 95% CI, 1.01–1.23; PFHxS: OR = 1.06; 95% CI, 1.02–1.11), and we observed a nonsignificant positive association with PFNA (OR = 1.32; 95% CI, 0.86–2.02). Conclusions: Our results, using cross-sectional data, are consistent with increased odds of ADHD in children with higher serum PFC levels. Given the extremely prevalent exposure to PFCs, follow-up of these data with cohort studies is needed

Hospital Variation in Utilization of Life‐Sustaining Treatments among Patients with Do Not Resuscitate Orders

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/1/hesr12651_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/2/hesr12651.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/3/hesr12651-sup-0001-AuthorMatrix.pd

A youth empowerment intervention to prevent childhood obesity: design and methods for a cluster randomized trial of the H2GO! program

BACKGROUND: Reducing sugar-sweetened beverage (SSB) consumption is a promising dietary target for childhood obesity prevention. This paper describes the design and methods of a cluster randomized trial of H2GO!, a youth empowerment intervention to prevent childhood obesity through reducing SSB consumption among a low-income, ethnically diverse sample of youth. METHODS: This cluster randomized controlled trial is an academic-community partnership with the Massachusetts Alliance of Boys and Girls Clubs (BGC). Ten BGC sites will be randomly assigned to the H2GO! intervention or a wait-list, usual care control. Eligible study participants will be N = 450 parent-child pairs (youth ages 9-12 years and their parents/caregivers) recruited from participating BGCs. The 6-week in-person H2GO! intervention consists of 12 group-based sessions delivered by BGC staff and youth-led activities. An innovative feature of the intervention is the development of youth-produced narratives as a strategy to facilitate youth empowerment and parental engagement. Child outcomes include measured body mass index z scores (zBMI), beverage intake, and youth empowerment. Parent outcomes include beverage intake and availability of SSBs at home. Outcomes will be measured at baseline and at 2, 6, and 12 months. With a 75% retention rate, the study is powered to detect a minimum group difference of 0.1 zBMI units over 12 months. DISCUSSION: Empowering youth may be a promising intervention approach to prevent childhood obesity through reducing SSB consumption. This intervention was designed to be delivered through BGCs and is hypothesized to be efficacious, relevant, and acceptable for the target population of low-income and ethnically diverse youth. TRIAL REGISTRATION: ClinicalTrials.gov NCT04265794 . Registered 11 February 2020