Individual and community-level determinants of mental and physical health after the deepwater horizon oil spill: findings from the gulf States population survey.

The 2010 Deepwater Horizon oil spill had enormous consequences on the environment. Prevalence of mental and physical health conditions among Gulf residents after the disaster, however, are still being assessed. The Gulf State Population Survey (GSPS) was a representative survey of 38,361 residents in four Gulf States and was conducted from December 2010 to December 2011. Analysis of the GSPS data showed that differences in individual characteristics and direct or indirect exposure to the disaster drove the individual-level variation in health outcomes (mental distress, physical distress, and depression). Direct exposure to the disaster itself was the most important determinant of health after this event. Selected county-level characteristics were not found to be significantly associated with any of our health indicators of interest. This study suggests that in the context of an overwhelming event, persons who are most directly affected through direct exposure should be the primary focus of any public health intervention effort.

A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data.

Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal-level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed. The method used to align disparate datasets must be accounted for when subsequently modeling the aligned data. Here we consider the case where kriging is used to align datasets in point-to-point and point-to-areal misalignment problems when the response variable is non-normally distributed. If the relationship is modeled using generalized linear models, the additional uncertainty induced from using the kriging mean as a covariate introduces a Berkson error structure. In this article, we develop a pseudo-penalized quasi-likelihood algorithm to account for the additional uncertainty when estimating regression parameters and associated measures of uncertainty. The method is applied to a point-to-point example assessing the relationship between low-birth weights and PM2.5 levels after the onset of the largest wildfire in Florida history, the Bugaboo scrub fire. A point-to-areal misalignment problem is presented where the relationship between asthma events in Florida's counties and PM2.5 levels after the onset of the fire is assessed. Finally, the method is evaluated using a simulation study. Our results indicate the method performs well in terms of coverage for 95% confidence intervals and naive methods that ignore the additional uncertainty tend to underestimate the variability associated with parameter estimates. The underestimation is most profound in Poisson regression models.

Awareness of kidney disease among US adults: Findings from the 2011 behavioral risk factor surveillance system.

BACKGROUND: The prevalence of chronic kidney disease as measured by biomarkers is increasing, but the recognition for this condition remains low in the USA. Little is known about the awareness of kidney disease at the state level. METHODS: Data from 490,302 adults aged 18 years or older in all 50 states as well as the District of Columbia who participated in the 2011 Behavioral Risk Factor Surveillance System were analyzed. Kidney disease diagnosis, a measure of individual awareness, was ascertained by participants' self-report in the telephone survey. Prevalence ratios of self-reported kidney disease in subpopulations were estimated and tested using log-linear regression analyses with a robust variance estimator. RESULTS: The unadjusted prevalence of self-reported kidney disease was estimated to be 2.5%. After adjustment for age and all other selected covariates, Hispanics had a higher prevalence than non-Hispanic whites (adjusted prevalence ratio 1.2, 95% CI 1.0-1.4). Persons who were unemployed (adjusted prevalence ratio 1.4, 95% CI 1.2-1.5) had a higher prevalence than those who were employed. Persons who had hypertension (adjusted prevalence ratio 1.9, 95% CI 1.7-2.1), diabetes (adjusted prevalence ratio 1.7, 95% CI 1.5-1.8), cardiovascular disease (coronary heart disease, myocardial infarction or stroke; adjusted prevalence ratio 1.5, 95% CI 1.4-1.6) or cancer (adjusted prevalence ratio 1.5, 95% CI 1.3-1.6) had a higher prevalence of self-reported kidney disease than those without these conditions. CONCLUSION: The overall awareness of kidney disease was low in the general population. Efforts are needed to promote the awareness and early detection of kidney disease in public health services and clinical practice.

Age adjustment of diabetes prevalence: use of 2010 U.S. Census data.

BACKGROUND: There is a growing interest in using the 2010 U.S. Census data for age adjustment after the Census data are officially released. This report discusses the rationale, procedures, demonstrations, and caveats of age adjustment using the 2010 U.S. Census data. METHODS: Empirical data from the Behavioral Risk Factor Surveillance System and the 2010 U.S. Census age composition were used in demonstrations of computing the age-adjusted prevalence of diagnosed diabetes by race/ethnicity, across various geographic regions, and over time. RESULTS: The use of the 2010 U.S. Census data yielded higher age-adjusted prevalence of diagnosed diabetes than using the 2000 projected US population data. The differences persisted across geographic regions, among racial/ethnic groups, and over time. Sixteen age compositions were generated to facilitate the use of the 2010 Census data in age adjustment. The SAS survey procedures and SUDAAN software programs yielded similar age-adjusted prevalence estimates of diagnosed diabetes. CONCLUSIONS: Using the 2010 U.S. Census data tends to yield a higher age-adjusted measure than using the 2000 projected U.S. population data. Consistent use of a standard population and age composition is recommended once they are chosen for age adjustment.

Estimated generalized least squares in spatially misaligned regression models with Berkson error.

In environmental studies, relationships among variables that are misaligned in space are routinely assessed. Because the data are misaligned, kriging is often used to predict the covariate at the locations where the response is observed. Using kriging predictions to estimate regression parameters in linear regression models introduces a Berkson error, which induces a covariance structure that is challenging to estimate. In addition, if the parameters associated with kriging (e.g. trend surface parameters and spatial covariance parameters) are estimated, then an additional uncertainty is introduced. We characterize the total measurement error as part of a broader class of Berkson error models and develop an estimated generalized least squares estimator using estimated covariance parameters. In working with the induced model, we fully account for the error structure and estimate the covariance parameters using likelihood-based methods. We provide insight into when it is important to fully account for the covariance structure induced from the different error sources. We assess the performance of the estimators using simulation and illustrate the methodology using publicly available data from the US Environmental Protection Agency.

A comparison of errors in variables methods for use in regression models with spatially misaligned data.

When a response variable Y is measured on one set of points and a spatially varying predictor variable X is measured on a different set of points, X and Y have different supports and thus are spatially misaligned. To draw inference about the association between X and Y , X is commonly predicted at the points for which Y is observed, and Y is regressed on the predicted X. If X is predicted using kriging or some other smoothing approach, use of the predicted instead of the true (unobserved) X values in the regression results in unbiased estimates of the regression parameters. However, the naive standard errors of these parameters tend to be too small. In this article, two simulation studies are used to compare methods for providing appropriate standard errors in this spatial setting. Three of the methods are extended to the change-of-support case where X is observed at points, but Y is observed for areal units, and these approaches are also compared via simulation.

Linking health and environmental data in geographical analysis: it's so much more than centroids.

Programs and studies increasingly use existing data from multiple sources (e.g., surveillance systems, health registries, or governmental agencies) for analysis and inference. These data usually have been collected on different geographical or spatial units, with each varying from the ones of interest. Combining such disparate data creates statistical challenges. Florida's efforts to move toward implementing the Centers for Disease Control and Prevention (CDC)'s Environmental Public Health Tracking (EPHT) program aptly illustrate these concerns, which are typical of studies designed to measure the association between environmental and health outcomes. In this paper, we develop models of spatial associations between myocardial infarctions (MIs) and ambient ozone levels in Florida during August 2005 and use these models to illustrate the problems that can occur when making inferences from aggregated data, the concept of spatial support, and the importance of correct uncertainty assessment. Existing data on hospital discharges and emergency department visits were obtained from Florida's Agency for Health Care Administration. Environmental data were obtained from Florida's Department of Environmental Protection; sociodemographic data were obtained from the US Census Bureau; and data from CDC's Behavioral Risk Factor Surveillance System were used to provide additional information on other risk factors. We highlight the opportunities and challenges associated with combining disparate spatial data for EPHT analyses. We compare the results from two different approaches to data linkage, focusing on the need to account for spatial scale and the support of spatial data in the analysis. We use geographically weighted regression, not as a visual mapping tool, but as an inferential tool designed to indicate the need for spatial coefficients, a test that cannot be made by using the majority of Bayesian models. Finally, we use geostatistical simulation methods for uncertainty analysis to demonstrate its importance in models with predicted covariates. Our focus is on relatively simple methods and concepts that can be implemented with ESRI's(®) ArcGIS(®) software.

Assessing the association between environmental impacts and health outcomes: a case study from Florida.

The Centers for Disease Control and Prevention (CDC) created the Environmental Public Health Tracking (EPHT) program to integrate hazard monitoring, exposure, and health effects surveillance into a cohesive tracking network. Part of Florida's effort to move toward implementation of EPHT is to develop models of the spatial and temporal association between myocardial infarctions (MIs) and ambient ozone levels in Florida. Existing data were obtained from Florida's Agency for Health Care Administration, Florida's Department of Environmental Protection, the U.S. Census Bureau, and CDC's Behavioral Risk Factor Surveillance System. These data were linked by both ignoring spatial support and using block kriging, a support-adjusted approach. The MI data were indirectly standardized by age, race/ethnicity, and sex. The state of Florida was used as the comparison standard to compute the MI standardized event ratio (SER) for each county and each month. After the data were linked, global models were used initially to relate MIs to ambient ozone levels, adjusting for covariates. The global models provide an estimated relative MI SER for the state. Realizing that the association in MIs and ozone might change across locations, local models were used to estimate the relative MI SER for each county, again adjusting for covariates. Results differed, depending on whether the spatial support was ignored or accounted for in the models. The opportunities and challenges associated with EPHT analyses are discussed and future directions highlighted.

The effect of the 1995 heat wave in Chicago on all-cause and cause-specific mortality.

OBJECTIVES: We sought to reexamine the effects of the 1995 Chicago heat wave on all-cause and cause-specific mortality, including mortality displacement, using advanced time-series analysis methods. METHODS: We used Poisson regression with penalized regression splines to model excess mortality and mortality displacement over a 50-day period centered on the day in which the heat wave temperature peaked, adjusting for meteorological and other variables. We controlled for temporal trends by using daily mortality data during 1993-1997. We estimated relative risks (RRs) with reference to the first day of the 50-day period. RESULTS: We estimated that there were 692 excess deaths from June 21, 1995, to August 10, 1995; 26% of these deaths were owing to mortality displacement. RR for all-cause mortality on the day with peak mortality was 1.74 (95% confidence interval=1.67, 1.81). Risk of heat-related death was significantly higher among Blacks, and mortality displacement was substantially lower. CONCLUSIONS: The 1995 Chicago heat wave substantially effected all-cause and cause-specific mortality, but mortality displacement was limited. Mortality risks and displacement affected Blacks disproportionally. Appropriately targeted interventions may have a tangible effect on life expectancy.

Exposures to air pollutants during pregnancy and preterm delivery.

The association between preterm delivery (PTD) and exposure to air pollutants has recently become a major concern. We investigated this relationship in Incheon, Republic of Korea, using spatial and temporal modeling to better infer individual exposures. The birth cohort consisted of 52,113 singleton births in 2001-2002, and data included residential address, gestational age, sex, birth date and order, and parental age and education. We used a geographic information system and kriging methods to construct spatial and temporal exposure models. Associations between exposure and PTD were evaluated using univariate and multivariate log-binomial regressions. Given the gestational age, birth date, and the mother's residential address, we estimated each mother's potential exposure to air pollutants during critical periods of the pregnancy. The adjusted risk ratios for PTD in the highest quartiles of the first trimester exposure were 1.26 [95% confidence interval (CI), 1.11-1.44] for carbon monoxide, 1.27 (95% CI, 1.04-1.56) for particulate matter with aerodynamic diameter < or = 10 microm, 1.24 (95% CI, 1.09-1.41) for nitrogen dioxide, and 1.21 (95% CI, 1.04-1.42) for sulfur dioxide. The relationships between PTD and exposures to CO, NO2, and SO2 were dose dependent (p < 0.001, p < 0.02, p < 0.02, respectively) . In addition, the results of our study indicated a significant association between air pollution and PTD during the third trimester of pregnancy. In conclusion, our study showed that relatively low concentrations of air pollution under current air quality standards during pregnancy may contribute to an increased risk of PTD. A biologic mechanism through increased prostaglandin levels that are triggered by inflammatory mediators during exposure periods is discussed.

Use of the spatial scan statistic to identify geographic variations in late stage colorectal cancer in California (United States).

OBJECTIVES: To identify geographic variations in colorectal cancer by stage at diagnosis in California using a descriptive analysis coupled with a spatial analysis and to discuss methodological considerations concerning the spatial statistical method. METHODS: We analyzed 59,076 colorectal cancer cases diagnosed in California from 1996 to 2000 by logistic regression and by a spatial scan statistic to identify areas with a higher and lower relative risk of late-stage colorectal cancer. RESULTS: In California, 57% of overall cases of colorectal cancer were diagnosed at a late stage. Californians diagnosed with late-stage colorectal cancer were more likely to be Hispanic and living in areas of lower socioeconomic status. The spatial scan identified two areas where the observed number of late-stage cancer was different than the number expected from the distribution in the rest of the state. CONCLUSIONS: Spatial scan analyses can complement descriptive statistics, but results must be interpreted with consideration of factors that affect the ability to detect meaningful differences such as the number of events observed, accuracy in geocoding rural versus urban addresses, and the difficulty of adjusting for covariates.

Report from the CDC: mental health of women in postwar Afghanistan.

More than two decades of war and a culture that has denied women freedom of movement, access to healthcare, and education have affected the mental health status of Afghan women more than that of men. In 2002, the Centers for Disease Control and Prevention (CDC) conducted a national population-based mental health survey in Afghanistan. The prevalence of symptoms of depression was 73% (standard error [SE] 8.15) and 59% (SE 5.59), of symptoms of anxiety was 84% (SE 2.98) and 59% (SE 8.65), and of posttraumatic stress disorder (PTSD) was 48% (SE 6.19) and 32% (SE 4.22) for female and male respondents, respectively. Mean scores for social functioning were lower for women (52.00 [SE 2.77]) than for men (66.63 [SE 3.92]). Women had significantly lower mental health status and poorer social functioning than did men. Results of our survey underscore the need for financial donors and healthcare planners to address the current lack of mental healthcare resources, facilities, and trained mental healthcare professionals in Afghanistan and to establish mental health services directed at the specific needs of women. This study highlights the negative impact that war, restrictions in freedoms, and socioeconomic hardship have had on the mental health and social functioning of women in Afghanistan.

The mental health of expatriate and Kosovar Albanian humanitarian aid workers.

The mental health consequences of exposure to traumatic events and the risk factors for psychological morbidity among expatriate and Kosovar Albanian humanitarian aid workers have not been well studied. In June 2000, we used standardised screening tools to survey 285 (69.5%) of 410 expatriate aid workers and 325 (75.8%) of 429 Kosovar Albanian aid workers from 22 humanitarian organizations that were implementing health programmes in Kosovo. The mean number of trauma events experienced by expatriates was 2.8 (standard deviation: 2.7) and by Kosovar staff 3.2 (standard deviation: 2.8). Although only 1.1% of expatriate and 6.2% of Kosovar aid workers reported symptoms consistent with the diagnosis for post-traumatic stress disorder, 17.2% and 16.9%, respectively, reported symptoms satisfying the definition of depression. Regression analysis demonstrated that the number of trauma events experienced was significantly associated with depression for the two sets of workers. Organisational support services may be an important mediating factor and should be targeted at both groups.

Mental health, social functioning, and feelings of hatred and revenge of Kosovar Albanians one year after the war in Kosovo.

A cross-sectional cluster sample survey was conducted in June 2000 in Kosovo to assess the prevalence of mental health problems associated with traumatic experiences, feelings of hatred and revenge, and the level of social functioning among Kosovar Albanians approximately 1 year after the end of the war. Findings of the second cross-sectional survey were compared with those from our 1999 mental health survey in Kosovo. Included in the survey were 1399 Kosovar Albanians aged 15 years or older living in 593 randomly selected households across Kosovo. Twenty-five percent of respondents reported PTSD symptoms, compared with 17.1% in 1999. The MOS-20 social functioning score improved to 69.8 from 29.5 in 1999. In the 2000 survey 54% of men felt hatred toward the Serbs, compared with 88.7% in 1999.

Spatial prediction of counts and rates.

In this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor. Prediction methods are illustrated and compared through a case study based on real data and through a detailed simulation study. The paper emphasizes a better understanding of the strengths and weaknesses of each approach.

Mental health status of human rights workers, Kosovo, June 2000.

Human rights workers in humanitarian relief settings may be exposed to traumatic events that put them at risk for psychiatric morbidity. We conducted a cross-sectional survey in June 2000 to study the prevalence of psychiatric morbidity among 70 expatriate and Kosovar Albanian staff collecting human nights data in Kosovo. Among those surveyed, elevated levels of anxiety, depression, and posttraumatic stress disorder symptoms were found in 17.1, 8.6, and 7.1% respectively. Multiple regression analysis revealed that human rights workers at risk for elevated anxiety symptoms were those who had worked with their organization longer than 6 months, those who had experienced an armed attack, and those who experienced local hostility. Our study indicates that human rights organizations should consider mental health assessment, care, and prevention programs for their staff.

Helical CT with sagittal and coronal reconstructions: accuracy for detection of diaphragmatic injury.

OBJECTIVE: The objectives of our study were to determine the accuracy of single-detector helical CT (including coronal and sagittal reconstructions) for the diagnosis of traumatic diaphragmatic injury, establish measurements for the thickness of the normal diaphragmatic crus, and describe an additional sign of diaphragmatic injury: active arterial extravasation of contrast material at the level of the diaphragm. MATERIALS AND METHODS: The CT scans of 25 patients with surgically proven diaphragmatic injury and 22 patients with surgically confirmed uninjured diaphragms were blindly reviewed by five thoracic radiologists. Sagittal and coronal reconstructions were performed for 20 of the 25 patients with a proven diaphragmatic injury and for all the patients without a diaphragmatic injury. Scans were evaluated for findings suggestive of diaphragmatic injury and for associated injuries. Reviewers scored the usefulness of the reconstructed images for establishing the final diagnosis. Measurements of the right and left crura were performed to establish a threshold measurement that would enable radiologists to discriminate between a normal diaphragm and an injured diaphragm. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of helical CT were 84%, 77%, 81%, 81%, and 83%, respectively. Scans showing active arterial extravasation of contrast material enabled reviewers to correctly identify diaphragmatic injury in two patients. Reconstructed images confirmed the correct diagnosis in three patients but supported an incorrect diagnosis in two. The mean thickness of the diaphragmatic crura (right and left) was not significantly greater in patients with an injured diaphragm than in those with an uninjured diaphragm. CONCLUSION: Helical CT shows good sensitivity, specificity, and accuracy for the diagnosis of diaphragmatic injury. Coronal and sagittal reconstructions are of limited use in establishing or refuting this diagnosis. Active arterial extravasation of contrast material near the diaphragm should raise suspicion for injury. Crus measurements cannot be used to reliably distinguish between injured and uninjured diaphragms.