Frank C. Curriero

Fine particulate air pollution and mortality in 20 U.S. Cities, 1987-1994

BACKGROUND Air pollution in cities has been linked to increased rates of mortality and morbidity in developed and developing countries. Although these findings have helped lead to a tightening of air-quality standards, their validity with respect to public health has been questioned. METHODS We assessed the effects of five major outdoor-air pollutants on daily mortality rates in 20 of the largest cities and metropolitan areas in the United States from 1987 to 1994. The pollutants were particulate matter that is less than 10 microm in aerodynamic diameter (PM10), ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. We used a two-stage analytic approach that pooled data from multiple locations. RESULTS After taking into account potential confounding by other pollutants, we found consistent evidence that the level of PM10 is associated with the rate of death from all causes and from cardiovascular and respiratory illnesses. The estimated increase in the relative rate of death from all causes was 0.51 percent (95 percent posterior interval, 0.07 to 0.93 percent) for each increase in the PM10 level of 10 microg per cubic meter. The estimated increase in the relative rate of death from cardiovascular and respiratory causes was 0.68 percent (95 percent posterior interval, 0.20 to 1.16 percent) for each increase in the PM10 level of 10 microg per cubic meter. There was weaker evidence that increases in ozone levels increased the relative rates of death during the summer, when ozone levels are highest, but not during the winter. Levels of the other pollutants were not significantly related to the mortality rate. CONCLUSIONS There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses. These findings strengthen the rationale for controlling the levels of respirable particles in outdoor air.

The Association Between Extreme Precipitation and Waterborne Disease Outbreaks in the United States, 1948–1994

OBJECTIVES Rainfall and runoff have been implicated in site-specific waterborne disease outbreaks. Because upward trends in heavy precipitation in the United States are projected to increase with climate change, this study sought to quantify the relationship between precipitation and disease outbreaks. METHODS The US Environmental Protection Agency waterborne disease database, totaling 548 reported outbreaks from 1948 through 1994, and precipitation data of the National Climatic Data Center were used to analyze the relationship between precipitation and waterborne diseases. Analyses were at the watershed level, stratified by groundwater and surface water contamination and controlled for effects due to season and hydrologic region. A Monte Carlo version of the Fisher exact test was used to test for statistical significance. RESULTS Fifty-one percent of waterborne disease outbreaks were preceded by precipitation events above the 90th percentile (P = .002), and 68% by events above the 80th percentile (P = .001). Outbreaks due to surface water contamination showed the strongest association with extreme precipitation during the month of the outbreak; a 2-month lag applied to groundwater contamination events. CONCLUSIONS The statistically significant association found between rainfall and disease in the United States is important for water managers, public health officials, and risk assessors of future climate change.

The built environment and obesity: A systematic review of the epidemiologic evidence

We completed a systematic search of the epidemiologic literature on built environment and obesity and identified 63 relevant papers, which were then evaluated for the quality of between-study evidence. We were able to classify studies into one of two primary approaches for defining place and corresponding geographic areas of influence: those based on contextual effects derived from shared pre-determined administrative units and those based on individually unique geographic buffers. The 22 contextual papers evaluated 80 relations, 38 of which did not achieve statistical significance. The 15 buffer papers evaluated 40 relations, 24 of which did not achieve statistical significance. There was very little between-study similarity in methods in both types of approaches, which prevented estimation of pooled effects. The great heterogeneity across studies limits what can be learned from this body of evidence.

Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors

BackgroundSpatial variation in patterns of disease outcomes is often explored with techniques such as cluster detection analysis. In other types of investigations, geographically varying individual or community level characteristics are often used as independent predictors in statistical models which also attempt to explain variation in disease outcomes. However, there is a lack of research which combines geographically referenced exploratory analysis with multilevel models. We used a spatial scan statistic approach, in combination with predicted block group-level disease patterns from multilevel models, to examine geographic variation in prostate cancer grade and stage at diagnosis.ResultsWe examined data from 20928 Maryland men with incident prostate cancer reported to the Maryland Cancer Registry during 1992–1997. Initial cluster detection analyses, prior to adjustment, indicated that there were four statistically significant clusters of high and low rates of each outcome (later stage at diagnosis and higher histologic grade of tumor) for prostate cancer cases in Maryland during 1992–1997. After adjustment for individual case attributes, including age, race, year of diagnosis, patterns of clusters changed for both outcomes. Additional adjustment for Census block group and county-level socioeconomic measures changed the cluster patterns further.ConclusionsThese findings provide evidence that, in locations where adjustment changed patterns of clusters, the adjustment factors may be contributing causes of the original clusters. In addition, clusters identified after adjusting for individual and area-level predictors indicate area of unexplained variation, and merit further small-area investigations.

Antibiotic-Resistant Enterococci and Fecal Indicators in Surface Water and Groundwater Impacted by a Concentrated Swine Feeding Operation

Background The nontherapeutic use of antibiotics in swine feed can select for antibiotic resistance in swine enteric bacteria. Leaking swine waste storage pits and the land-application of swine manure can result in the dispersion of resistant bacteria to water sources. However, there are few data comparing levels of resistant bacteria in swine manure–impacted water sources versus unaffected sources. Objectives The goal of this study was to analyze surface water and groundwater situated up and down gradient from a swine facility for antibiotic-resistant enterococci and other fecal indicators. Methods Surface water and groundwater samples (n = 28) were collected up and down gradient from a swine facility from 2002 to 2004. Fecal indicators were isolated by membrane filtration, and enterococci (n = 200) were tested for susceptibility to erythromycin, tetracycline, clindamycin, virginiamycin, and vancomycin. Results Median concentrations of enterococci, fecal coliforms, and Escherichia coli were 4- to 33-fold higher in down-gradient versus up-gradient surface water and groundwater. We observed higher minimal inhibitory concentrations for four antibiotics in enterococci isolated from down-gradient versus up-gradient surface water and groundwater. Elevated percentages of erythromycin- (p = 0.02) and tetracycline-resistant (p = 0.06) enterococci were detected in down-gradient surface waters, and higher percentages of tetracycline- (p = 0.07) and clindamycin-resistant (p < 0.001) enterococci were detected in down-gradient groundwater. Conclusions We detected elevated levels of fecal indicators and antibiotic-resistant enterococci in water sources situated down gradient from a swine facility compared with up-gradient sources. These findings provide additional evidence that water contaminated with swine manure could contribute to the spread of antibiotic resistance.

High-Density Livestock Operations, Crop Field Application of Manure, and Risk of Community-Associated Methicillin-Resistant Staphylococcus aureus Infection in Pennsylvania

IMPORTANCE Nearly 80% of antibiotics in the United States are sold for use in livestock feeds. The manure produced by these animals contains antibiotic-resistant bacteria, resistance genes, and antibiotics and is subsequently applied to crop fields, where it may put community members at risk for antibiotic-resistant infections. OBJECTIVE To assess the association between individual exposure to swine and dairy/veal industrial agriculture and risk of methicillin-resistant Staphylococcus aureus (MRSA) infection. DESIGN, SETTING, AND PARTICIPANTS A population-based, nested case-control study of primary care patients from a single health care system in Pennsylvania from 2005 to 2010. Incident MRSA cases were identified using electronic health records, classified as community-associated MRSA or health care-associated MRSA, and frequency matched to randomly selected controls and patients with skin and soft-tissue infection. Nutrient management plans were used to create 2 exposure variables: seasonal crop field manure application and number of livestock animals at the operation. In a substudy, we collected 200 isolates from patients stratified by location of diagnosis and proximity to livestock operations. MAIN OUTCOMES AND MEASURES Community-associated MRSA, health care-associated MRSA, and skin and soft-tissue infection status (with no history of MRSA) compared with controls. RESULTS From a total population of 446,480 patients, 1539 community-associated MRSA, 1335 health care-associated MRSA, 2895 skin and soft-tissue infection cases, and 2914 controls were included. After adjustment for MRSA risk factors, the highest quartile of swine crop field exposure was significantly associated with community-associated MRSA, health care-associated MRSA, and skin and soft-tissue infection case status (adjusted odds ratios, 1.38 [95% CI, 1.13-1.69], 1.30 [95% CI, 1.05-1.61], and 1.37 [95% CI, 1.18-1.60], respectively); and there was a trend of increasing odds across quartiles for each outcome (P ≤ .01 for trend in all comparisons). There were similar but weaker associations of swine operations with community-associated MRSA and skin and soft-tissue infection. Molecular testing of 200 isolates identified 31 unique spa types, none of which corresponded to CC398 (clonal complex 398), but some have been previously found in swine. CONCLUSIONS AND RELEVANCE Proximity to swine manure application to crop fields and livestock operations each was associated with MRSA and skin and soft-tissue infection. These findings contribute to the growing concern about the potential public health impacts of high-density livestock production.

Revealing the microscale spatial signature of dengue transmission and immunity in an urban population

It is well-known that the distribution of immunity in a population dictates the future incidence of infectious disease, but this process is generally understood at individual or macroscales. For example, herd immunity to multiple pathogens has been observed at national and city levels. However, the effects of population immunity have not previously been shown at scales smaller than the city (e.g., neighborhoods). In particular, no study has shown long-term effects of population immunity at scales consistent with the spatial scale of person-to-person transmission. Here, we use the location of dengue patients’ homes in Bangkok with the serotype of the infecting pathogen to investigate the spatiotemporal distribution of disease risk at small spatial scales over a 5-y period. We find evidence for localized transmission at distances of under 1 km. We also observe patterns of spatiotemporal dependence consistent with the expected impacts of homotypic immunity, heterotypic immunity, and immune enhancement of disease at these distances. Our observations indicate that immunological memory of dengue serotypes occurs at the neighborhood level in this large urban setting. These methods have broad applications to studying the spatiotemporal structure of disease risk where pathogen serotype or genetic information is known.

Livestock Density as Risk Factor for Livestock-associated Methicillin-Resistant Staphylococcus aureus, the Netherlands

The risk for livestock-associated MRSA increases with increasing density of pigs and calves.

Mixture models for quantitative HIV RNA data

Clinical investigators are increasing their use of quantitative determinations of HIV viral load in their study populations. The distributions of these measures may be highly skewed, left-censored, and with an extra spike below the detection limit of the assay. We recommend use of a mixture model in this situation, with two sets of explanatory covariates. We extend this model to incorporate multiple measures across time, and to employ shared parameters as a way of increasing model efficiency and parsimony. Data from a cohort of HIV-infected men are used to illustrate these features, and simulations are performed to assess the utility of shared parameters.

A Composite Likelihood Approach to Semivariogram Estimation

This article proposes the use of estimating functions based on composite likelihood for the estimation of isotropic as well as geometrically anisotropic semivariogram parameters. The composite likelihood approach is objective, eliminating the specification of distance lags and lag tolerances associated with the commonly used moment estimator. Extensions to the geometric anisotropy case include a parameterized transformed distance function, which eliminates the subjective estimation of the parameters of geometric anisotropy. The composite likelihood approach requires no matrix inversions and the estimators are shown to be consistent in a fashion similar to maximum likelihood and restricted maximum likelihood but without reliance on strong distributional assumptions. Predictions based on composite likelihood estimates performed very well using isotropic and geometric anisotropic simulated data and compared favorably to predictions based on the traditional approach in the isotropic case. Comparisons were also made using data collected on iron-ore measurements where previous analyses determined a geometric anisotropic semivariogram model to be appropriate.