Effect of spatial resolution on cluster detection: a simulation study

BackgroundAggregation of spatial data is intended to protect privacy, but some effects of aggregation on spatial methods have not yet been quantified.MethodsWe generated 3,000 spatial data sets and evaluated power of detection at 12 different levels of aggregation using the spatial scan statistic implemented in SaTScan v6.0.ResultsPower to detect clusters decreased from nearly 100% when using exact locations to roughly 40% at the coarsest level of spatial resolution.ConclusionAggregation has the potential for obfuscation

Incorporating data on residential history for Disease Mapping

When studying the relationship between an individual’s location and the acquisition of disease, the location to use is not always clear. When location at exposure is different from that at diagnosis, the latter may not represent the relevant information. While time and location of exposure are often unknown, residential history of cases can substantially inform a spatial analysis. In spatial surveillance, spatial data on cases are often used to detect and locate subareas of the study region with higher or lower risk of disease. Current literature has adapted detection methods to incorporate residential history of cases where available. We extend a disease mapping method to incorporate such data. Through simulations we show that our method is more accurate at identifying a localized increased risk of disease when compared to mapping when only location at diagnosis is considered

On zeroes in sign and signed rank tests

When zeroes (or ties within pairs) occur in data being analyzed with a sign test or a signed rank test, nonparametric methods textbooks and software consistently recommend that the zeroes be deleted and the data analyzed as though zeroes did not exist. This advice is not consistent with the objectives of the majority of applications. In most settings a better approach would be to view the tests as testing hypotheses about a population median. There are relatively simple p-values available that are consistent with this viewpoint of the tests. These methods produce tests with good properties for testing a different (often more appropriate) set of hypotheses than those addressed by tests that delete the zeroes

Comparison of censoring assumptions to reduce bias in tuberculosis treatment cohort analyses.

ObjectiveObservational tuberculosis cohort studies are often limited by a lack of long-term data characterizing survival beyond the initial treatment outcome. Though Cox proportional hazards models are often applied to these data, differential risk of long-term survival, dependent on the initial treatment outcome, can lead to violations of model assumptions. We evaluate the performance of two alternate censoring approaches on reducing bias in treatment effect estimates.DesignWe simulate a typical multidrug-resistant tuberculosis cohort study and use Cox proportional hazards models to assess the relationship of an aggressive treatment regimen with hazard of death. We compare three assumptions regarding censored observations to determine which produces least biased treatment effect estimates: conventional non-informative censoring, an extension of short-term survival informed by literature, and incorporation of predicted long-term vital status.ResultsThe treatment regimen's protective effect on death is consistently underestimated by the conventional censoring method, up to 7.6%. Models using the two alternative censoring techniques produce treatment effect estimates consistently stronger and less biased than the conventional method, underestimating the treatment effect by less than 2.4% across all scenarios.ConclusionsWhen model assumptions are violated, alternative censoring techniques can more accurately estimate associations between treatment and long-term survival. In multidrug-resistant tuberculosis cohort analyses, this bias reduction may yield more accurate and, larger effect estimates. This bias reduction can be achieved through use of standard statistical procedures with a simple re-coding of the censoring indicator

Disease Mapping with Spatially Uncertain Data

We present a disease mapping method that accounts for spatially uncertain data by informatively weighting the locations of interest. This method is applied to programmatic tuberculosis data collected over three years in Lima, Peru, with the goal of identifying potential hotspots of drug-resistance transmission. The flexibility of this method, which accommodates any general weighting scheme, allows us to examine the affects of different assumptions regarding the uncertainty present in the data

Association of neighborhood greenness with self-perceived stress, depression and anxiety symptoms in older U.S adults

Abstract Background Neighborhood environment, such as green vegetation, has been shown to play a role in coping with stress and mental ill health. Yet, epidemiological evidence of the association between greenness and mental health is inconsistent. Methods We examined whether living in green space is associated with self-perceived stress, depressive and anxiety symptoms in a nationally representative, longitudinal sample of community-dwelling older adults (N = 4118; aged 57–85 years) in the United States. We evaluated perceived stress, depression and anxiety symptoms using the Cohen’s Perceived Stress Scale, the Center for Epidemiological Studies – Depression, and the Hospital Anxiety and Depression Scale − anxiety subscale, respectively. Greenness was assessed for each participant using the Normalized Difference Vegetation Index at 250-m resolution, as well as a buffer of 1000-m. We conducted longitudinal analyses to assess the associations between greenness and mental health upon adjusting for confounders (e.g., education), and to examine potential mediation and effect modification. Results An interquartile range (0.25 point) increase in contemporaneous greenness was significantly associated with 0.238 unit (95% CI: − 0.346, − 0.130) and 0.162 unit (95% CI: − 0.271, − 0.054) decrease in the perceived stress in base and multivariable models, respectively. The magnitude of the association was similar or even stronger when examining summer (− 0.161; 95% CI: − 0.295, − 0.027) and annual average of greenness (− 0.188; 95% CI: − 0.337, − 0.038), as well as greenness buffer of 1000-m. The greenness-stress association was partially mediated by physical activity (15.1% mediated), where increased greenness led to increased physical activity and less stress, and by history of respiratory diseases (− 3.8% mediated), where increased greenness led to increased respiratory disease and more stress. The association was also significantly modified by race, social support, physical function, socioeconomic status, and region. While greenness was not significantly associated with anxiety and depressive scores across all participants, significant inverse associations were found for Whites participants, and for individuals with higher socioeconomic status, who were physically active, as compared to their counterparts. Conclusion We found a direct association of greenness with perceived stress among older adults, and an indirect association mediated through physical activity and respiratory disease history. Our study findings warrant further examination of the mediation and modification of the greenness-mental health association

Additional file 1: of Association of neighborhood greenness with self-perceived stress, depression and anxiety symptoms in older U.S adults

Table S1. Descriptive statistics of the greenness measures. Table S2. Pearson correlations of neighborhood variables. Table S3. Mean difference (95% CI) in self-perceived stress, anxiety and depression symptoms associated with an interquartile-range increase in greenness. Table S4. Difference (95% CI) in symptoms of mental ill health associated with an interquartile-range increase in contemporaneous greenness in 1,000-m buffer zones. Table S5. Mean difference (95% CI) in self-perceived stress, anxiety and depression symptoms associated with tertiles of greenness. (DOCX 33 kb

Spatial measurement errors in the field of spatial epidemiology

Background: Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data. Methods: Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review. Results: We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed. Conclusion: Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors