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We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.

Objectives Examining the spatial patterns of diffuse large B-cell lymphoma (DLBCL) incidence and residential proximity to toxic release locations may provide insight regarding environmental and sociodemographic risk factors. Methods We linked and geocoded cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry with population data from the US Census and the Environmental Protection Agency's Toxics Release Inventory. We conducted cluster analyses and constructed Poisson regression models to assess DLBCL incidence as a function of mean distance to the toxic release sites. Results In total, 3851 incident DLBCL cases occurred among adults residing in Georgia between 1999 and 2008. Significant focal clustering was observed around 57% of ethylene oxide sites, 5% of benzene sites, 9% of tetrachloroethylene sites, 7% of styrene sites, 10% of formaldehyde sites, 5% of trichloroethylene sites, and 10% of all release sites. Mean distance to sites was significantly associated with DLBCL risk for all chemicals. Conclusions Proximity to Toxics Release Inventory sites can be linked to increased DLBCL risk as assessed through focal clustering and Poisson regression, and confirmatory studies using geospatial mapping can aid in further specifying risk factors for DLBCL.

Background: Antibiotic resistant bacteria like community-onset methicillin resistant Staphylococcus aureus (CO-MRSA) have continued to cause infections in children at alarming rates and are associated with health disparities. Geospatial analyses of individual and area level data can enhance disease surveillance and identify socio-demographic and geographic indicators to explain CO-MRSA disease transmission patterns and risks.Methods: A case control epidemiology approach was undertaken to compare children with CO-MRSA to a noninfectious condition (unintentional traumatic brain injury (uTBI)). In order to better understand the impact of place based risks in developing these types of infections, data from electronic health records (EHR) were obtained from CO-MRSA cases and compared to EHR data from controls (uTBI). US Census data was used to determine area level data. Multi-level statistical models were performed using risk factors determined a priori and geospatial analyses were conducted and mapped.Results: From 2002–2010, 4,613 with CO-MRSA and 34,758 with uTBI were seen from two pediatric hospitals in Atlanta, Georgia. Hispanic children had reduced odds of infection; females and public health insurance were more likely to have CO-MRSA. Spatial analyses indicate significant ‘hot spots’ for CO-MRSA and the overall spatial cluster locations, differed between CO-MRSA cases and uTBI controls.Conclusions: Differences exist in race, age, and type of health insurance between CO-MRSA cases compared to noninfectious control group. Geographic clustering of cases is distinct from controls, suggesting placed based factors impact risk for CO-MRSA infection.

Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM(2.5)) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM(2.5) concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7–13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m(3) increase in the 3-day average of PM(2.5) levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM(2.5) for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.

Currently, no federal policies exist in the United States regarding private wells; this authority is devolved to states. This study inventoried state-level policies governing private wells in the United States in order to identify the topics addressed by each state, division of responsibilities across state agencies, and geographic differences in policy comprehensiveness. From May to August 2018, two independent reviewers conducted an online search followed by directly contacting state agencies (98% response) to identify all state-level policies in the United States that directly reference private wells. The search, updated in April 2018, confirmed the existing water policy list and identified 23 additional policies. Policies were then coded according to nine not-mutually-exclusive classifications. The results indicate that all states had at least one policy addressing private well drilling or construction. Significant geographic differences exist in maintenance related policies. In conclusion, although drilling and construction safety are addressed by each state, some policy domains are addressed inconsistently across states, and other policy domains are absent in most states.

Moran’s index is a statistic that measures spatial autocorrelation, quantifying the degree of dispersion (or spread and properties) of components in some location/area. Recognizing that a single Moran’s statistic may not give a sufficient summary of the spatial autocorrelation measure, local spatial statistics have been gaining popularity. Accordingly, we propose to partition the area and compute the Moran’s statistic of each subarea. Patterns between the local neighbors are unveiled that would not otherwise be apparent. We consider the measures of Moran’s statistics while incorporating a time factor under a simulated multilevel Poisson phenomena where the dependence among the subareas is captured by the rate of increase of the phenomena over time, starting with a common factor in the scale. Our Moran’s statistics are calculated from an explicit algorithm in a Markov chain Monte Carlo simulation setting and further analyzed. The main consequence of our results is that trends and density of point patterns are captured from data and can serve as indices of local patterns. Results are compared to Geary’s statistics.

Background There are few longitudinal data on the risk factors and mediators of racial disparities in sepsis among community- dwelling US adults. Methods This is a longitudinal study of adult participants in the 1999–2005 National Health Interview Survey with data linked to the 1999–2011 National Death Index. We utilized National Vital Statistics System’s ICD-10 schema to define septicemia deaths (A40-A41), utilizing influenza and pneumonia deaths (J09-J11) and other causes of death as descriptive comparators. All statistics utilized survey design variables to approximate the US adult population. Results Of 206 691 adult survey participants, 1523 experienced a septicemia death. Factors associated with a >2-fold larger hazard of septicemia death included need for help with activities of daily living; self-reported “poor” and “fair” general health; lower education; lower poverty index ratio; self-reported emphysema, liver condition, stroke, and weak or failing kidneys; numerous measures of disability; general health worse than the year prior; >1 pack per day cigarette use; and higher utilization of health care. Blacks had age- and sex-adjusted hazards that were higher for septicemia deaths (hazard ratio [HR], 1.92; 95% confidence interval [CI], 1.65–2.23) than for other causes of death (HR, 1.32; 95% CI, 1.25–1.38). The strongest mediators of the septicemia disparity included self-reported general health condition, family income-poverty ratio, and highest education level achieved. Conclusions In this cohort, the major risk factors for septicemia death were similar to those for other causes of death, there was approximately a 2-fold black-white disparity in septicemia deaths, and the strongest mediators of this disparity were across domains of socioeconomic status.

Heterogeneous exposure to mosquitoes determines an individual’s contribution to vector-borne pathogen transmission. Particularly for dengue virus (DENV), there is a major difficulty in quantifying human-vector contacts due to the unknown coupled effect of key heterogeneities. To test the hypothesis that the reduction of human out-of-home mobility due to dengue illness will significantly influence population-level dynamics and the structure of DENV transmission chains, we extended an existing modeling framework to include social structure, disease-driven mobility reductions, and heterogeneous transmissibility from different infectious groups. Compared to a baseline model, naïve to human pre-symptomatic infectiousness and disease-driven mobility changes, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring; a model including mobility change alone predicted a 15.5% increase compared to the baseline model. At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission (R, the fraction of secondary cases predicted to be generated by an individual) by symptomatic individuals (up to -62%) at the expense of an increase in the relevance of their home (up to +40%). An individual’s positive contribution to R could be predicted by a GAM including a non-linear interaction between an individual’s biting suitability and the number of mosquitoes in their home (>10 mosquitoes and 0.6 individual attractiveness significantly increased R). We conclude that the complex fabric of social relationships and differential behavioral response to dengue illness cause the fraction of symptomatic DENV infections to concentrate transmission in specific locations, whereas asymptomatic carriers (including individuals in their pre-symptomatic period) move the virus throughout the landscape. Our findings point to the difficulty of focusing vector control interventions reactively on the home of symptomatic individuals, as this approach will fail to contain virus propagation by visitors to their house and asymptomatic carriers., Author summary Human mobility patterns can play an integral role in vector-borne disease dynamics by characterizing an individual’s potential contacts with disease-transmitting vectors. Dengue virus is transmitted by a sedentary vector, but human mobility allows individuals to have contact with mosquitoes at their home and other houses they frequent (their activity space). When accounting for the decreased mobility of symptomatic dengue cases in an agent-based simulation model, however, we found a severely diminished role of the activity space in onward transmission. Those who received the majority of their mosquito contacts outside their home experienced decreases in expected bites and onward transmission when mobility changes were accounted for. Onward transmission was driven by a synergistic relationship between the number of mosquitoes in an individual’s home and their biting suitability, where even those with the highest biting suitability would have limited contribution to transmission given a low number of household mosquitoes. Reactive vector control, which often targets symptomatic cases, could be effective for slowing onward transmission from these cases, but will fail to control virus transmission due to the disproportionate contribution of asymptomatic infections.

Although climate change occurs alongside other anthropogenic ecosystem impacts, little is known about how sea-surface temperature variability influences the ecotoxicology of persistent organic pollutants (POPs). We analyzed POP contaminant levels, and stable isotopes δ15N and δ13C as measures of trophic position, in eggs collected from the Gulf of Alaska and Bering Sea between 1999 and 2010 from two similar avian species with different trophic positions: common murres (Uria aalge) and thick-billed murres (Uria lomvia). The ebb and flow of the Pacific Decadal Oscillation (PDO), a long-lived El Nino-like pattern of climate variability in the Pacific Ocean, predicted both trophic position and polychlorinated biphenyl (PCB) levels in thick-billed murres, but not in common murres. There was a similar pattern of association of the PDO with organochlorine pesticide levels in thick-billed murres, but not in common murres. The magnitude of association in thick-billed murres of PDO with the level of a specific PCB congener was a function of the number of chlorine groups on the PCB congener. Although this statistical analysis does not account for all factors contributing to climate variation, this contrast between the species suggests that facultative changes in foraging behavior, reflected in trophic position, can determine how POPs flow through and thereby alter ecosystems under climate change.

The Netherlands underwent a large Q fever outbreak between 2007 and 2009. In this paper, we study spatial and temporal Coxiella burnetii exposure trends during this large outbreak as well as validate outcomes against other published studies and provide evidence to support hypotheses on the causes of the outbreak. To achieve this, we develop a framework using a dose-response model to translate acute Q fever case incidence into exposure estimates. More specifically, we incorporate a geostatistical model that accounts for spatial and temporal correlation of exposure estimates from a human Q fever dose-response model to quantify exposure trends during the outbreak. The 2051 cases, with the corresponding age, gender and residential addresses, reside in the region with the highest attack rates during the outbreak in the Netherlands between 2006 and 2009. We conclude that the multiyear outbreak in the Netherlands is caused by sustained release of infectious bacteria from the same sources, which suggests that earlier implementation of interventions may have prevented many of the cases. The model predicts the risk of infection and acute symptomatic Q fever from multiple exposure sources during a multiple-year outbreak providing a robust, evidence-based methodology to support decision-making and intervention design.

Background Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality. Methods We obtained average concentrations for ten air pollutants (p = 10) across a 12 km grid (n = 253) covering Atlanta, Georgia for 2002–2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics. Results We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution. Conclusion Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

There is strong evidence of a temporal relationship between virus infection in pregnant women and birth outcome., Zika virus infection emerged as a public health emergency after increasing evidence for its association with neurologic disorders and congenital malformations. In Salvador, Brazil, outbreaks of acute exanthematous illness (AEI) attributed to Zika virus, Guillain-Barré syndrome (GBS), and microcephaly occurred in 2015. We investigated temporal correlations and time lags between these outbreaks to identify a common link between them by using epidemic curves and time series cross-correlations. Number of GBS cases peaked after a lag of 5–9 weeks from the AEI peak. Number of suspected cases of microcephaly peaked after a lag of 30–33 weeks from the AEI peak, which corresponded to time of potential infections of pregnant mothers during the first trimester. These findings support the association of GBS and microcephaly with Zika virus infection and provide evidence for a temporal relationship between timing of arboviral infection of pregnant women during the first trimester and birth outcome.

Most host-parasite systems exhibit remarkable heterogeneity in the contribution to transmission of certain individuals, locations, host infectious states, or parasite strains. While significant advancements have been made in the understanding of the impact of transmission heterogeneity in epidemic dynamics and parasite persistence and evolution, the knowledge base of the factors contributing to transmission heterogeneity is limited. We argue that research efforts should move beyond considering the impact of single sources of heterogeneity and account for complex couplings between conditions with potential synergistic impacts on parasite transmission. Using theoretical approaches and empirical evidence from various host-parasite systems, we investigate the ecological and epidemiological significance of couplings between heterogeneities and discuss their potential role in transmission dynamics and the impact of control.

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state.

BACKGROUND Engagement in care is central to reducing mortality for HIV-infected persons and achieving the White House National AIDS Strategy of 80% viral suppression in the US by 2020. Where an HIV-infected person lives impacts his or her ability to achieve viral suppression. Reliable transportation access for healthcare may be a key determinant of this place-suppression relationship. METHODS ZIP code tabulation areas (ZCTAs) were the units of analysis. We used geospatial and ecologic analyses to examine spatial distributions of neighborhood-level variables (eg, transportation accessibility) and associations with: (1) community linkage to care, and (2) community viral suppression. Among Atlanta ZCTAs with data for newly diagnosed HIV cases (2006-2010), we used Moran I to evaluate spatial clustering and linear regression models to evaluate associations between neighborhood variables and outcomes. RESULTS In 100 ZCTAs with 8413 newly diagnosed HIV-positive residents, a median of 60 HIV cases were diagnosed per ZCTA during the 5-year period. We found significant clustering of ZCTAs with low linkage to care and viral suppression (Moran I = 0.218, P < 0.05). In high-poverty ZCTAs, a 10% point increase in ZCTA-level household vehicle ownership was associated with a 4% point increase in linkage to care (P = 0.02, R = 0.16). In low-poverty ZCTAs, a 10% point increase in ZCTA-level household vehicle ownership was associated with a 30% point increase in ZCTA-level viral suppression (P = 0.01, R = 0.08). CONCLUSIONS Correlations between transportation variables and community-level care linkage and viral suppression vary by area poverty level and provide opportunities for interventions beyond individual-level factors.

Biomass burning has a significant and adverse impact on air quality, climate change, and various ecosystems. The Hazard Mapping System (HMS) detects fires using data from multiple satellite sensors in order to maximize its fire detection rate. However, to date, the detection rate of the HMS fire product for small fires has not been well studied, especially using ground-based fire records. This paper utilizes the 2011 fire information compiled from ground observations and burn authorizations in Georgia to assess the comprehensiveness of the HMS active fire product. The results show that detection rates of the hybrid HMS increase substantially by integrating multiple satellite instruments. The detection rate increases dramatically from 3% to 80% with an increase in fire size from less than 0.02 km2 to larger than 2 km2, resulting in detection of approximately 12% of all recorded fires which represent approximately 57% of the total area burned. The spatial pattern of detection rates reveals that grid cells with high detection rates are generally located in areas where large fires occur frequently. The seasonal analysis shows that overall detection rates in winter and spring (12% and 13%, respectively) are higher than those in summer and fall (3% and 6%, respectively), mainly because of higher percentages of large fires (>0.19 km2) that occurred in winter and spring. The land cover analysis shows that detection rates are 2–7 percentage points higher in land cover types that are prone to large fires such as forestland and shrub land.

Hierarchical models for disease mapping. Hierarchical models provide a convenient framework to combine information across multiple observational units. Such models are particularly convenient for small area estimation of disease rates to improve statistical precision of estimates associated with small local sample sizes. We review the basic framework of model-based approaches for combining information across small administrative units through the use of random effects. Varying definitions of the random effects distribution allows one to combine information from all locations, neighboring locations, or a combination of both. Keywords: spatial statistics; small area estimation; conditional autoregression

In mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number, R0, for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that with three or more coupled heterogeneities, R0 under separable transmission depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit formulae for the cases of multivariate normal and multivariate log-normal distributions, showing that R0 can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The formulae, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results of the formulae are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results and discuss a setting in which coupled heterogeneities are likely to have a major impact on the value of R0. We also describe a rather surprising result: in a system with three heterogeneities, R0 can exhibit non-monotonic behavior with increasing levels of heterogeneity, in marked contrast to the familiar two heterogeneity setting in which R0 either increases or decreases with increasing heterogeneity.

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM2.5 with an R2 at 70% and generated reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5 exposure assessments and can also quantify the prediction errors.

Thomas A. Louis received his BA in Mathematics from Dartmouth College in 1966 and his Ph.D. in Mathematical Statistics from Columbia University in 1972. He served as a NIH Postdoctoral Fellow at Imperial College, London, from 1972–1973 and has held faculty positions at Boston University, Harvard School of Public Health, the University of Minnesota and the Bloomberg School of Public Health at Johns Hopkins University. In addition, he served as a Senior Statistical Scientist at the RAND Corporation, and as Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau. Tom has served as President of both the Eastern North American Region of the International Biometric Society and as President of the International Biometric Society. He is a Fellow of the American Statistical Association, the American Association for the Advancement of Science and the Institute of Mathematical Statistics. As of January 2018, Tom is Emeritus Professor, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University. In addition to his many statistical accomplishments, Tom is a strong advocate for professional development and a life-long lover of time on the water.

To set the stage for this review, I must confess that I am the sort of person thinks a lot about maps and public health (Waller, 2017), and the sort of person whose office features a framed print o...

Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0–146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82–86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0–79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8–1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission., Author summary Most dengue virus infections result in either no perceptible symptoms or symptoms that are so mild that they go undetected by surveillance systems. It is unclear how much these infections contribute to the overall transmission and burden of dengue. At an individual level, we show that people with asymptomatic infections are approximately 80% as infectious to mosquitoes as their symptomatic counterparts. At a population level, we show that approximately 88% of infections result from people who display no apparent symptoms at the time of transmission. These results suggest that individuals undetected by surveillance systems may be the primary reservoir of dengue virus transmission and that policy for dengue control and prevention must be revised accordingly.

Summary. When agencies release public-use data, they must be cognizant of the potential risk of disclosure associated with making their data publicly available. This issue is particularly pertinent in disease mapping, where small counts pose both inferential challenges and potential disclosure risks. While the small area estimation, disease mapping, and statistical disclosure limitation literatures are individually robust, there have been few intersections between them. Here, we formally propose the use of spatiotemporal data analysis methods to generate synthetic data for public use. Specifically, we analyze ten years of county-level heart disease death counts for multiple age-groups using a Bayesian model that accounts for dependence spatially, temporally, and between age-groups; generating synthetic data from the resulting posterior predictive distribution will preserve these dependencies. After demonstrating the synthetic data’s privacy-preserving features, we illustrate their utility by comparing estimates of urban/rural disparities from the synthetic data to those from data with small counts suppressed.

We analyzed counts of licensed bars, restaurants and off-premise alcohol outlets within 53 California cities from 2000–2013. Poisson models were used to assess overall space-time associations between outlet numbers and population size and median household income in local and spatially adjacent block groups. We then separated covariate effects into distinct spatial and temporal components (“decomposed” models). Overall models showed that densities of all outlet types were generally greatest within block groups that had lower income, were adjacent to block groups with lower income, had greater populations, and were adjacent to block groups that had greater populations. Decomposed models demonstrate that over time greater income was associated with increased counts of bars, and greater population was associated with greater numbers of restaurants and off-premise outlets. Acknowledging the many negative consequences for populations living in areas of high outlet density, these effects are a predictable and powerful social determinant of health.

Geographic patterns in stroke mortality have been studied as far back as the 1960s when a region of the southeastern United States became known as the “stroke belt” due to its unusually high rates. While stroke mortality rates are known to increase exponentially with age, an investigation of spatiotemporal trends by age group at the county level is daunting due to the preponderance of small population sizes and/or few stroke events by age group. In this paper, we implement a multivariate space–time conditional autoregressive model to investigate age-specific trends in county-level stroke mortality rates from 1973 to 2013. In addition to reinforcing existing claims in the literature, this work reveals that geographic disparities in the reduction of stroke mortality rates vary by age. More importantly, this work indicates that the geographic disparity between the “stroke belt” and the rest of the nation is not only persisting, but may in fact be worsening.

Multiple studies have developed surface PM2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM2.5. In this paper, we examined whether remotely sensed fire count data could improve PM2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM2.5 across the models considered. Cross validation (CV) generated an R2 of 0.69, a mean prediction error of 2.75 µg/m3, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m3, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m3, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM2.5 concentration estimation, especially in areas and seasons prone to fire events.

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

We briefly review five articles exploring spatiotemporal clustering of pediatric cancer cases in the state of Florida for the years 2000–2010. We review the general motivating question of interest (Are there clusters in the data?) and compare approaches with specific attention to the statistical quantification of this question, the epidemiologic insight gained about disease patterns, and potential policy responses to the collective results.

Background Missouri is an agriculturally intensive state, primarily growing corn and soybeans with additional rice and cotton farming in some southeastern counties. Communities located in close proximity to pesticide-treated fields are known to have increased exposure to pesticides and may be at increased risk of adverse birth outcomes. The study aims were to assess the relationship between county-level measures of crop-specific agricultural production and adverse birth outcomes in Missouri and to evaluate the most appropriate statistical methodologies for doing so. Methods Potential associations between county level data on the densities of particular crops and low birth weight and preterm births were examined in Missouri between 2004−2006. Covariates considered as potential confounders and effect modifiers included gender, maternal race/ethnicity, maternal age at delivery, maternal smoking, access to prenatal care, quarter of birth, county median household income, and population density. These data were analyzed using both standard Poisson regression models as well as models allowing for temporal and spatial correlation of the data. Results There was no evidence of an association between corn, soybean, or wheat densities with low birth weight or preterm births. Significant positive associations between both rice and cotton density were observed with both low birth weight and preterm births. Model results were consistent using Poisson and alternative models accounting for spatial and temporal variability. Conclusions The associations of rice and cotton with low birth weight and preterm births warrant further investigation. Study limitations include the ecological study design and limited available covariate information.

To estimate PM

Neglected tropical diseases (NTDs) are communicable diseases that occur under conditions of poverty and are concentrated almost exclusively in impoverished populations in the developing world. NTDs affect more than 1000 million people in tropical and subtropical countries, costing developing economies billions of dollars every year. Effective control of NTDs can be achieved with the use of large-scale delivery of single-dose preventive chemotherapy (PC) or intensified disease management (IDM) or both, as is the case for some diseases such as lymphatic filariasis, trachoma, and yaws. Several NTDs exhibit significant cutaneous manifestations that are associated with long-term disfigurement and disability, including Buruli ulcer (BU); cutaneous leishmaniasis (CL); leprosy; mycetoma; yaws; hydrocele and lymphoedema (resulting from lymphatic filariasis); and depigmentation, subcutaneous nodules, severe itching, and hanging groin (resulting from onchocerciasis). Skin examination offers an opportunity to screen people in the communities or children in schools to identify multiple conditions in a single visit. This common approach to skin diseases justifies the integrated delivery of health care interventions to both increase cost-effectiveness and expand coverage. WHO’s Department of Control of NTDs (WHO/NTD) plans to promote an integrated strategy for the skin NTDs requiring IDM. Targeting skin NTDs also provides a platform for treatment of common skin conditions and, therefore, has wider public health benefits. An informal panel of experts (writing this manuscript) was established to help develop guidance in support of the new WHO strategic direction and to develop a proposal for a change in policy for the integrated control and management of the skin NTDs. A symposium at the 2015 ASTMH meeting[1] initiated a discussion of opportunities around integration of surveillance and control of NTDs that affect the skin, but this paper moves these ideas forward and includes some initial recommendations about how these opportunities could be realised. We aim to provide specific pragmatic information and actual recommendations about potential surveillance and management approaches.

The history and development of public health was motivated, illustrated, and implemented with the use of maps. In this chapter, we provide a brief review of the use of maps and related concepts in public health spanning from medieval plague quarantine plans to remotely sensed satellite measures of air pollution. We begin with early maps of infectious disease (e.g., plague, yellow fever, and cholera) including an in-depth review of Dr. John Snow’s famed map of the 1854 London cholera epidemic. We also review the role of mapping in contemporary political debates regarding miasma versus contagion as underlying causes of disease and associated early public health responses such as sanitation and quarantine. We next highlight atlases of disease identifying, documenting, labeling, and mapping endemic areas of known diseases (e.g., yellow fever, cholera, and leprosy) across the globe . Finally, we outline the rise of quantification of observed patterns and the use of spatial statistical techniques to investigate epidemiologic hypotheses regarding geographic variations in disease risk and associations with potential local explanatory factors. Taken together, we find a rich history of mapping in the development, maturation, and modern implementation of public health science and practice.

Background. Melioidosis results from infection with Burkholderia pseudomallei and is associated with casefatality rates up to 40%. Early diagnosis and treatment with appropriate antimicrobials can improve survival rates. Fatal and nonfatal melioidosis cases were identified in Puerto Rico in 2010 and 2012, respectively, which prompted contact investigations to identify risk factors for infection and evaluate endemicity. Methods. Questionnaires were administered and serum specimens were collected from coworkers, neighborhood contacts within 250 m of both patients’ residences, and injection drug user (IDU) contacts of the 2012 patient. Serum specimens were tested for evidence of prior exposure to B. pseudomallei by indirect hemagglutination assay. Neighborhood seropositivity results guided soil sampling to isolate B. pseudomallei. Results. Serum specimens were collected from contacts of the 2010 (n = 51) and 2012 (n = 60) patients, respectively. No coworkers had detectable anti–B. pseudomallei antibody, whereas seropositive results among neighborhood contacts was 5% (n = 2) for the 2010 patient and 23% (n = 12) for the 2012 patient, as well as 2 of 3 IDU contacts for the 2012 case. Factors significantly associated with seropositivity were having skin wounds, sores, or ulcers (odds ratio [OR], 4.6; 95% confidence interval [CI], 1.2–17.8) and IDU (OR, 18.0; 95% CI, 1.6–194.0). Burkholderia pseudomallei was isolated from soil collected in the neighborhood of the 2012 patient. Conclusions. Taken together, isolation of B. pseudomallei from a soil sample and high seropositivity among patient contacts suggest at least regional endemicityof melioidosis in Puerto Rico. Increased awareness of melioidosis is needed to enable early case identification and early initiation of appropriate antimicrobial therapy.

The literature in Geographical Information Science and Statistical Science often contains calls for analysts to “think spatially” and to “think statistically”, respectively, in order to gain better insight into proposed hypotheses. A central element of these calls involves the development of a spatial intuition or a statistical intuition, i.e., conceptual ways of framing questions, incorporating available data, calculating quantitative summaries, interpreting outcomes, and providing empirical answers from either a spatial or a statistical perspective. In this paper, I draw on past experience to identify and illustrate the potential for “spatial statistical thinking”, that is, the development of a spatial statistical intuition. Several examples illustrate the potential for a more explicit development of such intuition drawing simultaneously from both spatial analysis and statistics. As a step in this direction, I stress the importance of maintaining a spatial conceptual framework in the application and interpretation of spatial statistical methods, i.e., the importance of spatial interpretation of spatial statistics.

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μg m−3, and RMSPE from 3.12 to 5.00 μg m−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ~20%. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

The concentration detection threshold (CDT) is the concentration of particles in solution beyond which a (serial dilution) assay detects particle presence. By our account, CDTs typically are not estimated but are fixed at some value. Setting a CDT to zero ( $$d=0$$ ) implies perfect detection, a common assumption, and setting $$d>0$$ gives results that are “denominated” in units of $$d$$ , i.e., are relative to the choice of $$d$$ . Using multiple, different serial dilution assays, each with its own CDT, we choose a “reference assay,” to which we assign a fixed CDT value, to obtain relative estimates of the remaining assays’ CDTs and the underlying particle concentration. We present the CDTs as a novel way to account for or to compare different serial dilution assays, “sensitivities”. We apply our methodology to data from four assays of the presence of bacterial (B. abortus) antibodies in the serum of elk in the Greater Yellowstone Ecosystem, where transmission of brucellosis—the disease ensuing from infection—to commercial livestock is managed by the Wyoming Game and Fish Department to avoid the primary symptom of abnormal fetal abortion. Results agree qualitatively with the more traditional notion of sensitivity as the true positive rate.

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

Background There is significant variation surrounding the indications, surgical approaches, and outcomes for children undergoing antireflux procedures (ARPs) resulting in geographic variation of care. Our purpose was to quantify this geographic variation in the utilization of ARPs in children. Methods A cross-sectional analysis of the 2009 Kid's Inpatient Database was performed to identify patients with gastroesophageal reflux disease or associated diagnoses. Regional surgical utilization rates were determined, and a mixed effects model was used to identify factors associated with the use of ARPs. Results Of the 148,959 patients with a diagnosis of interest, 4848 (3.3%) underwent an ARP with 2376 (49%) undergoing a laparoscopic procedure. The Northeast (2.0%) and Midwest (2.2%) had the lowest overall utilization of surgery, compared with the South (3.3%) and West (3.4%). After adjustment for age, case-mix, and surgical approach, variation persisted with the West and the South demonstrating almost two times the odds of undergoing an ARP compared with the Northeast. Surgical utilization rates are independent of state-level volume with some of the highest case volume states having surgical utilization rates below the national rate. In the West, the use of laparoscopy correlated with overall utilization of surgery, whereas surgical approach was not correlated with ARP use in the South. Conclusions Significant regional variation in ARP utilization exists that cannot be explained entirely by differences in patient age, race/ethnicity, case-mix, and surgical approach. In order to decrease variation in care, further research is warranted to establish consensus guidelines regarding indications for the use ARPs for children.

A carefully considered observational study estimates that up to 22% of infant deaths in sub-Saharan Africa could be prevented by improving air quality — a value much higher than previous estimates. A carefully considered observational study estimates that up to 22% of infant deaths in sub-Saharan Africa could be prevented by improving air quality — a value much higher than previous estimates.

The objective of this study was to describe the spatial risk-patterns of prematurity and low birth weight in Spain. A descriptive spatial analysis of births registered in the Spanish Vital Statistics during 2004–2008 using municipalities as the observation unit was carried out. Besag-York-Mollié autoregressive spatial models were adjusted using the Integrated Nested Laplace approximation to calculate relative risks and posterior probabilities of having very and moderate preterm or low weight newborns. Results were represented in maps to assess geographic risk-patterns. Spatial analysis shows geographical variations in the risk of adverse reproductive outcomes in Spain highlighting three main high-risk zones, namely, municipalities in Asturias, Madrid City and Murcia. The specific risk patterns identified on each zone suggests some differences regarding the potential underlying risk factors and specific areas for future research. A differential exposure during pregnancy to some risks potentially related to industry or agriculture and other contextual factors could underlie an unequal vulnerability to adverse reproductive outcomes in some Spanish regions. Fondo de Investigación Sanitaria (PI081330); Spanish Ministry of Science and Innovation (SEJ 2005/07679); CIBER en Epidemiología y Salud Pública (CIBERESP), Spain. Sí

BACKGROUND: An increased risk of non-Hodgkin lymphoma (NHL) has been observed among individuals with occupational exposure to benzene, but the risk among those living near benzene release sites has not been well described. METHODS: To investigate the spatial patterns of NHL incidence and the association between NHL incidence and distance to benzene release sites, the authors linked and geocoded data on benzene release sites in Georgia from 1988 to 1998 using the Environmental Protection Agency’s (EPA) Toxics Release Inventory (TRI), census tract level population statistics, and NHL incidence from the Georgia Comprehensive Cancer Registry (GCCR) from 1999 to 2008. Standardized incidence ratios were mapped by census tract, and a Poisson regression was performed on NHL and NHL subtype incidence data using the mean distance between the tract centroids and release sites as markers of exposure. Cluster analyses were conducted at the global, local, and focal levels. RESULTS: Poisson regression indicated that, for every mile the average distance to benzene release sites increased, there was an expected 0.31% decrease in the risk of NHL. Similar results were observed for all NHL subtypes analyzed. Clusters of NHL were spatially associated with benzene release sites located in metropolitan areas, but not with release sites in other areas of the state. CONCLUSIONS: NHL incidence was significantly higher in census tracts that were closer, on average, to benzene release sites. Additional studies are needed to examine spatial patterns of NHL incidence in other geographic regions and interactions between benzene and other exposures. Cancer 2013;119:3309-17. V C 2013 American Cancer Society.

type="main" xml:lang="es"> Dos objetivos importantes en el analisis de precios hedonicos son la prediccion de los precios de venta y la delimitacion de submercados basada en consideraciones geograficas y funcionales. Para hacer frente a estos objetivos, en este articulo se han aplicado modelos bayesianos con coeficientes espacialmente variables para realizar un analisis de los precios de venta de la vivienda en la ciudad de Toronto (Ontario). Se evaluo el desempeno del modelo y se determinaron los patrones de los submercados indicados por los procesos de coeficiente espacial. Los resultados muestran que los modelos de proceso espacial bayesiano predicen bien los precios de venta de viviendas, proporcionan inferencias utiles con respecto a la heterogeneidad de precios dentro de un mercado, y pueden especificarse de modo que incluyan las opiniones de los expertos del mercado.

Buruli ulcer, caused by Mycobacterium ulcerans, is highly endemic in West Africa. While the mode of transmission is unknown, many studies associate Buruli ulcer with different types of water exposure. We present results from the largest study to date to test for M. ulcerans in aquatic sites and identify environmental attributes associated with its presence. Environmental samples from 98 aquatic sites in the Greater Accra, Ashanti, and Volta regions of Ghana were tested for the presence of M. ulcerans DNA by polymerase chain reaction. The proportion of aquatic sites positive for M. ulcerans varied by region: Ashanti 66% (N = 39), Greater Accra 34% (N = 29), and Volta 0% (N = 30). We explored the spatial distribution of M. ulcerans positive and negative water bodies and found no significant clusters. We also determined both highly localized water attributes and broad scale remotely sensed land cover and terrain environmental characteristics associated with M. ulcerans presence through logistic regression. Our results concur with published results regarding conditions suitable for M. ulcerans growth and associations with Buruli ulcer disease burden with regards to water characteristics and disturbed environments, but differ from others with regards to spatial associations and topographic effects such as elevation and wetness. While our results suggest M. ulcerans is an environmental organism existing in a specific ecological niche, they also reveal variation in the elements defining this niche across the sites considered. In addition, despite the causal association between Buruli ulcer and M. ulcerans, we observed no significant statistical association between case reports of Buruli ulcer and presence of M. ulcerans in nearby waterbodies.