Spaces of Segregation and Health: Complex Associations for Black Immigrant and US-Born Mothers in New York City.

Black immigrants are a growing proportion of the Black population in the USA, and despite the fact that they now comprise nearly a quarter of Black urban residents, few studies address the relationships between racial segregation and maternal and birth outcomes among Black immigrants. In this study of birth outcomes among US-born and immigrant Black mothers in New York City between 2010 and 2014, we applied multilevel models, assessing the association between segregation (measured through a novel kernel-based measure of local segregation) and adverse birth outcomes (preterm birth (PTB) and low birth weight (LBW; < 2500 g)) among African-born, Caribbean-born, and US-born Black mothers. We found that African-born and Caribbean/Latin American-born Black mothers had a significantly lower incidence of PTB compared with US-born Black mothers (7.0 and 10.1, respectively, compared with 11.2 for US-born mothers). We also found disparities in the incidence of infant LBW by nativity, with the highest incidence among infants born to US-born mothers (10.9), compared with African-born (6.9) and Caribbean-born mothers (9.0). After adjusting for maternal (maternal age; higher rates of reported drug use and smoking) and contextual characteristics (neighborhood SES; green space access), we found that maternal residence in an area with high Black segregation increases the likelihood of PTB and LBW among US-born and Caribbean-born Black mothers. In contrast, the association between segregation and birth outcomes was insignificant for African-born mothers. Associations between tract-level socioeconomic disadvantage and birth outcomes also varied across groups, with only US-born Black mothers showing the expected positive association with risk of PTB and LBW.

Explained and unexplained racial and regional inequality in obesity prevalence in the United States.

Objective: There are substantial racial and regional disparities in obesity prevalence in the United States. This study partitioned the mean Body Mass Index (BMI) and obesity prevalence rate gaps between non-Hispanic blacks and non-Hispanic whites into the portion attributable to observable obesity risk factors and the remaining portion attributable to unobservable factors at the national and the state levels in the United States (U.S.) in 2010. Design: This study used a simulated micro-population dataset combining common information from the Behavioral Risk Factor Surveillance System and the U.S. Census data to obtain a reliable, large sample representing the adult populations at the national and state levels. It then applied a reweighting decomposition method to decompose the black-white mean BMI and obesity prevalence disparities at the national and state levels into the portion attributable to the differences in distribution of observable obesity risk factors and the remaining portion unexplainable with risk factors. Results: We found that the observable differences in distribution of known obesity risk factors explain 18.5% of the mean BMI difference and 20.6% of obesity prevalence disparities between non-Hispanic blacks and non-Hispanic whites. There were substantial variations in how much the differences in distribution of known obesity risk factors can explain black-white gaps in mean BMI (-67.7% to 833.6%) and obesity prevalence (-278.5% to 340.3%) at the state level. Conclusion: The results from this study demonstrate that known obesity risk factors explain a small proportion of the racial, ethnic and between-state disparities in obesity prevalence in the United States. Future etiologic studies are required to further understand the causal factors underlying obesity and racial, ethnic and geographic disparities.

Physical activity and chronic diseases among older people in a mid-size city in China: a longitudinal investigation of bipolar effects.

BACKGROUND: While previous studies have shown that regular physical activity can delay the onset of certain chronic diseases; less is known about the changes in physical activity practices following chronic disease diagnoses. China is experiencing a rapid aging transition, with physical activity an important routine in many older people's lives. This study utilizes the Health Belief Model to better understand the bidirectional relationships and bipolar effects between physical activity and chronic disease burden in Huainan City, a mid-sized city in China. METHODS: Longitudinal health survey data (2010-2015) from annual clinic visits for 3198 older people were obtained from a local hospital, representing 97% of the older population in three contiguous neighborhoods in Huainan City. The chronic diseases studied included obesity, hypertension, diabetes, hyperlipidemia, cardiovascular diseases, liver and biliary system diseases, and poor kidney function. Multilevel logistic regression was used to examine differences in physical activity levels across socio-demographic groups. Cox proportional hazards models were used to examine the impacts of physical activity practice levels on chronic disease onsets. Logistic regression was used to estimate the effects of chronic disease diagnosis on physical activity practice levels. RESULTS: The prevalence of chronic diseases increased with increasing age, among men, and those with a lower education. Older people who were physically active experienced a later onset of chronic disease compared to their sedentary counterparts, particularly for obesity and diabetes. Following diagnosis of a chronic disease, physically active older people were more likely to increase their physical activity levels, while sedentary older people were less likely to initiate physical activity, demonstrating bipolar health trajectory effects. CONCLUSIONS: Health disparities among older people may widen as the sedentary experience earlier onsets of chronic diseases and worse health trajectories, compared to physically active people. Future health education communication and programmatic interventions should focus on sedentary and less healthy older populations to encourage healthy aging. These lessons from China may be applied to other countries also experiencing an increasing aging population.

COVID-19 in nursing homes: Geographic diffusion and regional risk factors from January 1 to July 26, 2020 of the pandemic.

BACKGROUND: COVID-19 deaths in nursing homes accounted for 30.2% of all COVID-19 deaths in the United States during the early weeks (1-January to 26-July, 2020) of the pandemic. This study presents the geographic diffusion of COVID-19 cases and deaths in nursing homes during this time period, while also providing explanation of regional risk factors. METHODS AND FINDINGS: Nursing home COVID-19 data on confirmed cases (n = 173,452) and deaths (n = 46,173) were obtained from the Centers for Medicare and Medicaid Services. Weekly COVID-19 case counts were spatially smoothed to identify nursing homes in areas of high COVID-19 infection. Bivariate spatial autocorrelation was used to visualize High vs. Low-case counts and related deaths. Zero-inflated negative binomial models were estimated within Health and Human Service (HHS) Regions at three-week intervals to evaluate facility and area-level risk factors. The first reported nursing home resident to die of COVID-19 was in the state of Washington on 28-February, 2020. By 24-May, 2020 there were simultaneous epicenters in the Northeast (HHS Regions 1 and 2) and Midwest (HHS Region 5) with diffusion into the South (HHS Regions 4 and 6) from 15-June to 5-July, 2020. The case-fatality rate was highest from 25-May to 14-June, 2020 (30.9 deaths per 1000 residents); thereafter declining to 24.1 (15-June to 5-July, 2020) and 19.4 (6-July to 26-July, 2020) (overall case-fatality rate 1-January to 26-July = 26.6). Statistically significant risk factors for COVID-19 deaths were admission of patients with COVID-19 into nursing homes, staff confirmed infections and nursing shortages. COVID-19 deaths were likely to occur in nursing homes in high minority and non-English speaking neighborhoods and neighborhoods with a high proportion of households with disabilities. CONCLUSIONS: Enhanced communication between HHS regional administrators about "lessons learned" could provide receiving state health departments with timely information to inform clinical practice to prevent premature death in nursing homes in future pandemics.

Regional trends in diagnosis of advanced lung cancer in Michigan over 33 years.

Background: Lung cancer is the most common cancer killer worldwide. Nearly 80 percent of lung cancers are diagnosed at advanced stages. Lack of access to medical care and undwerutilized lung cancer screening are key reasons for advanced diagnoses. We sought to understand the regional differences in presentation of lung cancer across Michigan. Utilizing a comprehensive cancer registry over 33 years, our goal was to examine associations between sociodemographic patient factors and diagnoses at advanced stages. Methods: The Michigan Cancer Registry was queried from 1985 to 2018 to include all new diagnoses of non-small cell lung cancer (NSCLC) using International Classification of Diseases for Oncology (ICD-O) version 3 codes. NSCLC was categorized as early, regional and distant disease. Advanced disease was considered to be any disease that was regional or distant. NSCLC rates were calculated and mapped at the zip code level using the 2010 population as the denominator and spatial empirical Bayes methodology. Regional hospital service areas were constructed using travel time to treatment from the patient's zip code centroid. Logistic regression models were estimated to investigate the significance of rural vs. urban and travel time on level of disease at presentation. Kaplan-Meier and multivariate survival analysis was performed to evaluate the association between distance from the nearest medical center and length of survival controlling for known risk factors for lung cancer. Results: From 1985 to 2018, there were 141,977 patients in Michigan diagnosed with NSCLC. In 1985, men were 2.2 times more likely than women to be diagnosed but by 2018 women and men developed disease at equal rates. Mean age was 67.8 years. Among all patients with known stage of disease, 72.5% of patients were diagnosed with advanced disease. Regional and distant NSCLC rates were both higher in the northern parts of the state. Longer drive times in rural regions also significantly increased the likelihood of advanced NSCLC diagnoses, in particular regional lung cancer. Patients with longer drive times also experienced overall worse survival after controlling for other factors. Conclusions: Regional disparities exist in Michigan for diagnoses of NSCLC at advanced stages. Factors such as lack of screening in urban regions and distances to treating institutions in rural areas likely contribute to the increased likelihood of advanced NSCLC. Future interventions should target the specific needs of residents to detect disease at earlier stages and improve overall outcomes.

Spatial and spatio-temporal clusters of lung cancer incidence by stage of disease in Michigan, United States 1985-2018.

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

Lung Cancer and Air Quality in a Large Urban County in the United States.

Lung cancer is the leading cancer-related killer in the United States. The incidence varies geographically and may be affected by environmental pollutants. Our goal was to determine associations within time series for specific air pollutants and lung cancer cases over a 33-year period in Wayne County, Michigan, controlling for population change. Lung cancer data for Wayne County were queried from the Michigan Cancer Registry from 1985 to 2018. Air pollutant data were obtained from the United States Environmental Protection Agency from 1980 to 2018. Autoregressive distributed lag (ARDL) models were estimated to investigate time lags in years between specific air pollution levels and lung cancer development. A total of 58,866 cases of lung cancer were identified. The mean age was 67.8 years. Females accounted for 53 percent of all cases in 2018 compared to 44 percent in 1985. Three major clusters of lung cancer incidence were detected with the most intense clusters in downtown Detroit and the heavily industrialized downriver area. Sulfur dioxide (SO2) had the strongest statistically significant relationship with lung cancer, showing both short- and long-term effects (lag range, 1-15 years). Particulate matter (PM2.5) (lag range, 1-3 years) and nitrogen dioxide (NO2) (lag range, 2-4 years) had more immediate effects on lung cancer development compared to carbon monoxide (CO) (lag range, 5-6 years), hazardous air pollutants (HAPs) (lag range, 9 years) and lead (Pb) (lag range, 10-12 years), which had more long-term effects on lung cancer development. Areas with poor air quality may benefit from targeted interventions for lung cancer screening and reductions in environmental pollution.

Measuring geographic access to health care: raster and network-based methods.

BACKGROUND: Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. METHODS: We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan's Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. RESULTS: In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. CONCLUSIONS: Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Tsetse Fly Control in Kenya's Spatially and Temporally Dynamic Control Reservoirs: A Cost Analysis.

Human African trypanosomiasis (HAT) and animal African trypanosomiasis (AAT) are significant health concerns throughout much of sub-Saharan Africa. Funding for tsetse fly control operations has decreased since the 1970s, which has in turn limited the success of campaigns to control the disease vector. To maximize the effectiveness of the limited financial resources available for tsetse control, this study develops and analyzes spatially and temporally dynamic tsetse distribution maps of Glossina subgenus Morsitans populations in Kenya from January 2002 to December 2010, produced using the Tsetse Ecological Distribution Model. These species distribution maps reveal seasonal variations in fly distributions. Such variations allow for the identification of "control reservoirs" where fly distributions are spatially constrained by fluctuations in suitable habitat and tsetse population characteristics. Following identification of the control reservoirs, a tsetse management operation is simulated in the control reservoirs using capital and labor control inputs from previous studies. Finally, a cost analysis, following specific economic guidelines from existing tsetse control analyses, is conducted to calculate the total cost of a nationwide control campaign of the reservoirs compared to the cost of a nationwide campaign conducted at the maximum spatial extent of the fly distributions from January 2002 to December 2010. The total cost of tsetse management within the reservoirs sums to $14,212,647, while the nationwide campaign at the maximum spatial extent amounts to $33,721,516. This savings of $19,508,869 represents the importance of identifying seasonally dynamic control reservoirs when conducting a tsetse management campaign, and, in the process, offers an economical means of fly control and disease management for future program planning.

Lead Emissions and Population Vulnerability in the Detroit Metropolitan Area, 2006-2013: Impact of Pollution, Housing Age and Neighborhood Racial Isolation and Poverty on Blood Lead in Children.

This research investigates the relationships between airborne and depositional industrial lead emission concentrations modeled using Environmental Protection Agency's (EPA's) American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and childhood blood lead levels (BLL) in the Detroit Metropolitan Area (DMA) 2006-2013. Linear and mediation interaction regression models estimated the effects of older housing and airborne and depositional lead emission concentrations on black and white childhood BLLs, controlling for neighborhood levels of racial isolation and poverty-important social structures in the DMA. The results showed a direct relationship between airborne and depositional lead emissions and higher childhood BLL, after controlling for median housing age. Lead emissions also exacerbated the effect of older housing on black and white children's BLLs (indirect relationship), after controlling for social structures. Findings from this research indicate that black and white children exposed to lead-based paint/pipes in older housing are further impacted by industrial lead pollution that may lead to permanent neurological damage.

Outbreaks of foodborne Salmonella enteritidis in the United States between 1990 and 2015: An analysis of epidemiological and spatial-temporal trends.

OBJECTIVES: To evaluate the role of eggs and other food vehicles as risk factors associated with Salmonella enteritidis (SE) outbreaks in order to address the endemicity of SE infections in the USA. METHODS: We retrieved and analyzed data relating to all SE outbreaks reported to the Centers for Disease Control and Prevention (CDC) between 1990 and 2015. We then used descriptive and analytical statistical methods, including negative binomial regression models for the estimation of rate-ratios, to analyze the data. RESULTS: Analyses showed that egg-based dishes were the most common food vehicle associated with outbreaks of SE in the USA (273 cases [24%]); this was followed by several other food items, including meat (130 cases [11%]), vegetables (96 cases [8%]), chicken items (95 cases [8%]), dairy products (55 cases [5%]), and bakery items (8 cases [1%]). Compared to egg-based dishes, other food items such as meat (exp(ß) = 0.51, 95% CI 0.37, 0.69), chicken (exp(ß) = 0.42, 95% CI 0.30, 0.58), vegetables (exp(ß) = 0.41, 95% CI 0.29, 0.55), and dairy items (exp(ß) = 0.27, 95% CI 0.18, 0.40) were significantly associated with outbreaks of SE in the USA. Of 1144 SE outbreaks, 402 (35%) occurred in the Northeast region of the USA, followed by the South (253 [22%]), West (250 [22%]), and Midwestern regions (239 [21%]). CONCLUSIONS: Epidemiological and spatiotemporal trends analyses demonstrated that a significant proportions of Salmonella enteritidis outbreaks in the USA are attributed to food vehicles other than eggs. Our findings can be used to plan effective strategies to mitigate the increasing occurrence of foodborne SE outbreaks.

Effects of green buildings on employee health and productivity.

We investigated the effects of improved indoor environmental quality (IEQ) on perceived health and productivity in occupants who moved from conventional to green (according to Leadership in Energy and Environmental Design ratings) office buildings. In 2 retrospective-prospective case studies we found that improved IEQ contributed to reductions in perceived absenteeism and work hours affected by asthma, respiratory allergies, depression, and stress and to self-reported improvements in productivity. These preliminary findings indicate that green buildings may positively affect public health.

Use of a spatiotemporal approach for understanding preschoolers' playground activity.

This study used spatiotemporal hot-spot analysis to characterize physical activity on the childcare center playground. Preschool-aged children (N = 34) wore a GPS and accelerometer during 2-3 outdoor periods on one day. A spatiotemporal weights matrix was generated so that points within a specified distance in meters (space) and 3 min (time) were considered neighbors. The Getis-Ord G* statistic was calculated to detect locations of significant hot/cold spots in vector magnitude counts/15­sec. Hot/cold spots changed within a single outdoor period and between outdoor periods, highlighting the importance of time. This approach can be used to identify points of intervention during provided outdoor time.

Geographic analysis of low birthweight and infant mortality in Michigan using automated zoning methodology.

BACKGROUND: Infant mortality is a major public health problem in the State of Michigan and the United States. The primary adverse reproductive outcome underlying infant mortality is low birthweight. Visualizing and exploring the spatial patterns of low birthweight and infant mortality rates and standardized incidence and mortality ratios is important for generating mechanistic hypotheses, targeting high-risk neighborhoods for monitoring and implementing maternal and child health intervention and prevention programs and evaluating the need for health care services. This study investigates the spatial patterns of low birthweight and infant mortality in the State of Michigan using automated zone matching (AZM) methodology and minimum case and population threshold recommendations provided by the National Center for Health Statistics and the US Census Bureau to calculate stable rates and standardized incidence and mortality ratios at the Zip Code (n = 896) level. The results from this analysis are validated using SaTScan. Vital statistics birth (n = 370,587) and linked infant death (n = 2,972) records obtained from the Michigan Department of Community Health and aggregated for the years 2004 to 2006 are utilized. RESULTS: For a majority of Zip Codes the relative standard errors (RSEs) of rates calculated prior to AZM were greater than 20%. Spurious results were the result of too few case and birth counts. Applying AZM with a target population of 25 cases and minimum threshold of 20 cases resulted in the reconstruction of zones with at least 50 births and RSEs of rates 20-22% and below respectively, demonstrating the stability reliability of these new estimates. Other AZM parameters included homogeneity constraints on maternal race and maximum shape compactness of zones to minimize potential confounding. AZM identified areas with elevated low birthweight and infant mortality rates and standardized incidence and mortality ratios. Most but not all of these areas were also detected by SaTScan. CONCLUSION: Understanding the spatial patterns of low birthweight and infant deaths in Michigan was an important first step in conducting a geographic evaluation of the State's reported high infant mortality rates. AZM proved to be a useful tool for visualizing and exploring the spatial patterns of low birthweight and infant deaths for public health surveillance. Future research should also consider AZM as a tool for health services research.

Mediating medical risk factors in the residential segregation and low birthweight relationship by race in New York City.

This study explores mediating medical risk factors in the association between racial residential segregation (i.e., racial 'black' isolation) and low birthweight in New York City, adjusting for maternal and infant risk factors and neighborhood poverty. This race-specific cross-sectional multilevel study found that as racial isolation increased in neighborhoods, the odds of having a low birthweight infant also increased for African-American and White women living in these areas. Medical conditions that mediated the racial isolation and low birthweight relationship included chronic hypertension and pregnancy-related hypertension for African-American women and chronic hypertension and lung disease for White women. Although this study was limited by the quality of the birth certificate data, it does provide exploratory pathways by which medical risks and their sequelae are linked to neighborhood environments and reproductive vulnerability.

Mapping Multi-Disease Risk during El Niño: An Ecosyndemic Approach.

El Niño is a quasi-periodic pattern of climate variability and extremes often associated with hazards and disease. While El Niño links to individual diseases have been examined, less is known about the cluster of multi-disease risk referred to as an ecosyndemic, which emerges during extreme events. The objective of this study was to explore a mapping approach to represent the spatial distribution of ecosyndemics in Piura, Peru at the district-level during the first few months of 1998. Using geographic information systems and multivariate analysis, descriptive and analytical methodologies were employed to map disease overlap of 7 climate-sensitive diseases and construct an ecosyndemic index, which was then mapped and applied to another El Niño period as proof of concept. The main findings showed that many districts across Piura faced multi-disease risk over several weeks in the austral summer of 1998. The distribution of ecosyndemics were spatially clustered in western Piura among 11 districts. Furthermore, the ecosydemic index in 1998 when compared to 1983 showed a strong positive correlation, demonstrating the potential utility of the index. The study supports PAHO efforts to develop multi-disease based and interprogrammatic approaches to control and prevention, particularly for climate and poverty-related infections in Latin America and the Caribbean.

Impacts of Federally Funded State Obesity Programs on Adult Obesity Prevalence in the United States, 1998-2010.

OBJECTIVES: From 2000 to 2010, the Division of Nutrition, Physical Activity, and Obesity (DNPAO) at the Centers for Disease Control and Prevention (CDC) funded 37 state health departments to address the obesity epidemic in their states through various interventions. The objective of this study was to investigate the overall impacts of CDC-DNPAO statewide intervention programs on adult obesity prevalence in the United States. METHODS: We used a set of an individual-level, interrupted time-series regression and a quasi-experimental analysis to evaluate the overall effect of CDC-DNPAO intervention programs before (1998-1999) and after (2010) their implementation by using data from CDC's Behavioral Risk Factor Surveillance System. RESULTS: States that implemented the CDC-DNPAO program had a 2.4% to 3.8% reduction in the odds of obesity during 2000-2010 compared with states without the program. The effect of the CDC-DNPAO program varied by length of program implementation. A quasi-experimental analysis found that states with longer program implementation did not necessarily have lower odds of obesity than states with shorter program implementation. CONCLUSIONS: Statewide obesity interventions can contribute to reduced odds of obesity in the United States. Future research should evaluate the CDC-DNPAO programs in relation to their goals, objectives, and other environmental obesity risk factors to inform future interventions.

Adult obesity prevalence at the county level in the United States, 2000-2010: Downscaling public health survey data using a spatial microsimulation approach.

Obesity is a growing public health concern in the United States. There is a need to monitor obesity prevalence at the local level to intervene in place-specific ways. However, national public health surveys suppress the local geographic information of respondents due to small sample sizes and the protection of confidentiality. This study therefore, uses a spatial microsimulation approach to estimate obesity prevalence rates at the county level across the United States to visualize temporal, spatial and spatio-temporal changes from 2000 to 2010 for use in the monitoring of obesity prevalence. This method iteratively replicates the demographic characteristics of public health survey respondents with census data for those areas. Following, Local Moran's I was used to identify clusters of high and low obesity prevalence. The findings showed that obesity prevalence rose dramatically over the last decade with substantial variation across counties and states. Counties in Southern states, especially along the Mississippi River and Appalachian Mountains and counties containing or in proximity to Native American reservation sites showed elevated obesity prevalence rates across the decade. Counties in Midwestern states had higher obesity prevalence rates compared to counties in Western and Northeastern states. This study demonstrated the use of spatial microsimulation modeling as an alternative method to obtain reliable obesity prevalence rates at the local-level using existing health survey and census data.

Lead Emissions and Population Vulnerability in the Detroit (Michigan, USA) Metropolitan Area, 2006-2013: A Spatial and Temporal Analysis.

Objective: The purpose of this research is to geographically model airborne lead emission concentrations and total lead deposition in the Detroit Metropolitan Area (DMA) from 2006 to 2013. Further, this study characterizes the racial and socioeconomic composition of recipient neighborhoods and estimates the potential for IQ (Intelligence Quotient) loss of children residing there. Methods: Lead emissions were modeled from emitting facilities in the DMA using AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model). Multilevel modeling was used to estimate local racial residential segregation, controlling for poverty. Global Moran's I bivariate spatial autocorrelation statistics were used to assess modeled emissions with increasing segregation. Results: Lead emitting facilities were primarily located in, and moving to, highly black segregated neighborhoods regardless of poverty levels-a phenomenon known as environmental injustice. The findings from this research showed three years of elevated airborne emission concentrations in these neighborhoods to equate to a predicted 1.0 to 3.0 reduction in IQ points for children living there. Across the DMA there are many areas where annual lead deposition was substantially higher than recommended for aquatic (rivers, lakes, etc.) and terrestrial (forests, dunes, etc.) ecosystems. These lead levels result in decreased reproductive and growth rates in plants and animals, and neurological deficits in vertebrates. Conclusions: This lead-hazard and neighborhood context assessment will inform future childhood lead exposure studies and potential health consequences in the DMA.