Spatiotemporal Patterns of Deaths of Despair Across the U.S., 2000-2019.

INTRODUCTION: Deaths of despair (i.e., suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis) have been increasing over the past 2 decades. However, no large-scale studies have examined geographic patterns of deaths of despair in the U.S. This ecologic study identifies geographic and temporal patterns of individual and co-occurring clusters of deaths of despair. METHODS: All individuals aged ≥10 years who died in the U.S. between 2000 and 2019 and resided within the 48 contiguous states and Washington, District of Columbia were included (N=2,171,105). Causes of death were limited to deaths of despair, namely suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis. Univariate and multivariate space-time scan statistics were used to identify individual and co-occurring clusters with excess risk of deaths of despair. County-level RRs account for heterogeneity within each cluster. Analyses were conducted from late 2021 to early 2022. RESULTS: Six suicide clusters, four overdose clusters, nine liver disease clusters, and three co-occurring clusters of all three types of deaths were identified. A large portion of the western U.S., southeastern U.S., and Appalachia/rust belt were contained within the co-occurring clusters. The co-occurring clusters had average county RRs ranging from 1.17 (p<0.001) in the southeastern U.S. to 4.90 (p<0.001) in the western U.S. CONCLUSIONS: Findings support identifying and targeting risk factors common to all types of deaths of despair when planning public health interventions. Resources and policies that address all deaths of despair simultaneously may be beneficial for the areas contained within the co-occurring high-risk clusters.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data.

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

The impact of the national stay-at-home order on emergency department visits for suspected opioid overdose during the first wave of the COVID-19 pandemic.

BACKGROUND: Although national syndromic surveillance data reported declines in emergency department (ED) visits after the declaration of the national stay-at-home order for COVID-19, little is known whether these declines were observed for suspected opioid overdose. METHODS: This interrupted time series study used syndromic surveillance data from four states participating in the HEALing Communities Study: Kentucky, Massachusetts, New York, and Ohio. All ED encounters for suspected opioid overdose (n = 48,301) occurring during the first 31 weeks of 2020 were included. We examined the impact of the national public health emergency for COVID-19 (declared on March 14, 2020) on trends in ED encounters for suspected opioid overdose. RESULTS: Three of four states (Massachusetts, New York and Ohio) experienced a statistically significant immediate decline in the rate of ED encounters for suspected opioid overdose (per 100,000) after the nationwide public health emergency declaration (MA: -0.99; 95 % CI: -1.75, -0.24; NY: -0.10; 95 % CI, -0.20, 0.0; OH: -0.33, 95 % CI: -0.58, -0.07). After this date, Ohio and Kentucky experienced a sustained rate of increase for a 13-week period. New York experienced a decrease in the rate of ED encounters for a 10-week period, after which the rate began to increase. In Massachusetts after a significant immediate decline in the rate of ED encounters, there was no significant difference in the rate of change for a 6-week period, followed by an immediate increase in the ED rate to higher than pre-COVID levels. CONCLUSIONS: The heterogeneity in the trends in ED encounters between the four sites show that the national stay-at-home order had a differential impact on opioid overdose ED presentation in each state.

Disparities in Opioid Overdose Death Trends by Race/Ethnicity, 2018-2019, From the HEALing Communities Study.

Objectives. To examine trends in opioid overdose deaths by race/ethnicity from 2018 to 2019 across 67 HEALing Communities Study (HCS) communities in Kentucky, New York, Massachusetts, and Ohio. Methods. We used state death certificate records to calculate opioid overdose death rates per 100 000 adult residents of the 67 HCS communities for 2018 and 2019. We used Poisson regression to calculate the ratio of 2019 to 2018 rates. We compared changes by race/ethnicity by calculating a ratio of rate ratios (RRR) for each racial/ethnic group compared with non-Hispanic White individuals. Results. Opioid overdose death rates were 38.3 and 39.5 per 100 000 for 2018 and 2019, respectively, without a significant change from 2018 to 2019 (rate ratio = 1.03; 95% confidence interval [CI] = 0.98, 1.08). We estimated a 40% increase in opioid overdose death rate for non-Hispanic Black individuals (RRR = 1.40; 95% CI = 1.22, 1.62) relative to non-Hispanic White individuals but no change among other race/ethnicities. Conclusions. Overall opioid overdose death rates have leveled off but have increased among non-Hispanic Black individuals. Public Health Implications. An antiracist public health approach is needed to address the crisis of opioid-related harms. (Am J Public Health. 2021;111(10):1851-1854. https://doi.org/10.2105/AJPH.2021.306431).

Electronic cigarette use and risk of cigarette and smokeless tobacco initiation among adolescent boys: A propensity score matched analysis.

INTRODUCTION: Electronic cigarette (e-cigarette) use among adolescents is associated with increased risk of subsequent cigarette smoking initiation in observational research. However, the existing research was not designed to answer causal questions about whether adolescent e-cigarette users would have initiated cigarette smoking if they had never used e-cigarettes. The current study used a causal inference framework to identify whether male adolescent e-cigarette users were at increased risk of initiating cigarette smoking and smokeless tobacco (SLT) use, compared to similar boys who had never used e-cigarettes. METHODS: Boys from urban and Appalachian Ohio (N = 1220; ages 11-16 years at enrollment) reported use of e-cigarettes, cigarettes, and SLT at baseline and every six months for two years. A propensity score matching design was implemented, matching one e-cigarette user to two similar e-cigarette non-users. This analysis was completed in 25 multiple imputed datasets to account for missing data. Risk ratios (RRs) comparing risk of initiating cigarettes and SLT for e-cigarette users and nonusers were estimated. RESULTS: Compared to non-users, e-cigarette users were more than twice as likely to later initiate both cigarette smoking (RR = 2.71; 95% CI: 1.89, 3.87) and SLT (RR = 2.42; 95% CI: 1.73, 3.38). They were also more likely to become current (i.e., past 30-day) cigarette smokers (RR = 2.20; 95% CI: 1.33, 3.64) and SLT users (RR = 1.64; 95% CI: 1.01, 2.64). CONCLUSIONS: Adolescent boys who used e-cigarettes had increased risk of later initiating traditional tobacco products when compared to similar boys who had never used e-cigarettes.

Operationalizing and selecting outcome measures for the HEALing Communities Study.

BACKGROUND: The Helping to End Addiction Long-termSM (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. METHODS: Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. RESULTS: The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. CONCLUSIONS: The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.

A Community Development Program and Reduction in High-Cost Health Care Use.

Adverse housing and neighborhood conditions influence child health. The Healthy Neighborhoods Healthy Families community development initiative was established in 2008 to address housing, education, employment, and other neighborhood-level, child health-influencing factors on the south side of Columbus, Ohio, with the goal of improving child health and well-being. In this article, we discuss the path from advocacy to outcomes analysis in this initiative and assess changes in high-cost health care use by children in the target area over the first decade of implementation. Change in health care use was measured by using a difference-in-differences approach comparing emergency department visits, inpatient stays, and inpatient length of stay in the intervention neighborhood and a propensity score-matched, pooled comparator neighborhood in the same city. The baseline and follow-up periods were August 2008 to July 2010 and August 2015 to July 2017, respectively. Findings from this analysis reveal that compared to 2 pooled comparison neighborhoods, the intervention neighborhood trended, nonsignificantly, toward greater decreases in inpatient stays and emergency department visits and smaller increases in length of stays. These results suggest that our community development activities may be influencing health care use outcomes, but in the early years of the intervention relative changes are modest and are variable based on the definition of the intervention and comparator neighborhoods. Lessons learned in expanding from advocacy to analysis include the importance of building multidisciplinary teams that can apply novel approaches to analysis, moderating expectations, and retaining focus on the broader social context.

Impact of Outdoor Air Pollution on Indoor Air Quality in Low-Income Homes during Wildfire Seasons.

Indoor and outdoor number concentrations of fine particulate matter (PM2.5), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2) were monitored continuously for two to seven days in 28 low-income homes in Denver, Colorado, during the 2016 and 2017 wildfire seasons. In the absence of indoor sources, all outdoor pollutant concentrations were higher than indoors except for CO. Results showed that long-range wildfire plumes elevated median indoor PM2.5 concentrations by up to 4.6 times higher than outdoors. BC, CO, and NO2 mass concentrations were higher indoors in homes closer to roadways compared to those further away. Four of the homes with mechanical ventilation systems had 18% higher indoor/outdoor (I/O) ratios of PM2.5 and 4% higher I/O ratios of BC compared to other homes. Homes with exhaust stove hoods had PM2.5 I/O ratios 49% less than the homes with recirculating hoods and 55% less than the homes with no stove hoods installed. Homes with windows open for more than 12 hours a day during sampling had indoor BC 2.4 times higher than homes with windows closed. This study provides evidence that long-range wildfire plumes, road proximity, and occupant behavior have a combined effect on indoor air quality in low-income homes.

Contextual Factors Associated With County-Level Suicide Rates in the United States, 1999 to 2016.

Importance: Understanding geographic and community-level factors associated with suicide can inform targeted suicide prevention efforts. Objectives: To estimate suicide rates and trajectories, assess associated county-level contextual factors, and explore variation across the rural-urban continuum. Design, Setting, and Participants: This cross-sectional study included all individuals aged 25 to 64 years who died by suicide from January 1, 1999, to December 31, 2016, in the United States. Spatial analysis was used to map excess risk of suicide, and longitudinal random-effects models using negative binomial regression tested associations of contextual variables with suicide rates as well as interactions among county-level contextual variables. Data analyses were conducted between January 2019 and July 2019. Exposure: County of residence. Main Outcomes and Measures: Three-year county suicide rates during an 18-year period stratified by rural-urban location. Results: Between 1999 and 2016, 453 577 individuals aged 25 to 64 years died by suicide in the United States. Decedents were primarily male (349 082 [77.0%]) with 101 312 (22.3%) aged 25 to 34 years, 120 157 (26.5%) aged 35 to 44 years, 136 377 (30.1%) aged 45 to 54 years, and 95 771 (21.1%) aged 55 to 64 years. Suicide rates were higher and increased more rapidly in rural than in large metropolitan counties. The highest deprivation quartile was associated with higher suicide rates compared with the lowest deprivation quartile, especially in rural areas, although this association declined during the period studied (rural, 1999-2001: incidence rate ratio [IRR], 1.438; 95% CI, 1.319-1.568; P < .001; large metropolitan, 1999-2001: 1.208; 95% CI, 1.149-1.270; P < .001; rural, 2014-2016: IRR, 1.121; 95% CI, 1.032-1.219; P = .01; large metropolitan, 2014-2016: IRR, 0.942; 95% CI, 0.887-1.001; P = .06). The presence of more gun shops was associated with an increase in county-level suicide rates in all county types except the most rural (rural: IRR, 1.001; 95% CI, 0.999-1.004; P = .40; micropolitan: IRR, 1.005; 95% CI, 1.002-1.007; P < .001; small metropolitan: IRR, 1.010; 95% CI, 1.006-1.014; P < .001; large metropolitan: IRR, 1.012; 95% CI, 1.006-1.018; P < .001). High social capital was associated with lower suicide rates than low social capital (IRR, 0.917; 95% CI, 0.891-0.943; P < .001). High social fragmentation, an increasing percentage of the population without health insurance, and an increasing percentage of veterans in a county were associated with higher suicide rates (high social fragmentation: IRR, 1.077; 95% CI, 1.050-1.103; P < .001; percentage of population without health insurance: IRR, 1.005; 95% CI, 1.004-1.006; P < .001; percentage of veterans: IRR, 1.025; 95% CI, 1.021-1.028; P < .001). Conclusions and Relevance: This study found that suicide rates have increased across the nation and most rapidly in rural counties, which may be more sensitive to the impact of social deprivation than more metropolitan counties. Improving social connectedness, civic opportunities, and health insurance coverage as well as limiting access to lethal means have the potential to reduce suicide rates across the rural-urban continuum.

Top 10 Research Priorities in Spatial Lifecourse Epidemiology.

The International Initiative on Spatial Lifecourse Epidemiology (ISLE) convened its first International Symposium on Lifecourse Epidemiology and Spatial Science at the Lorentz Center in Leiden, Netherlands, 16­20 July 2018. Its aim was to further an emerging transdisciplinary field: Spatial Lifecourse Epidemiology. This field draws from a broad perspective of scientific disciplines including lifecourse epidemiology, environmental epidemiology, community health, spatial science, health geography, biostatistics, spatial statistics, environmental science, climate change, exposure science, health economics, evidence-based public health, and landscape ecology. The participants, spanning 30 institutions in 10 countries, sought to identify the key issues and research priorities in spatial lifecourse epidemiology. The results published here are a synthesis of the top 10 list that emerged out of the discussion by a panel of leading experts, reflecting a set of grand challenges for spatial lifecourse epidemiology in the coming years. https://doi.org/10.1289/EHP4868.

Mapping suicide mortality in Ohio: A spatial epidemiological analysis of suicide clusters and area level correlates.

Previous studies have investigated spatial patterning and associations of area characteristics with suicide rates in Western and Asian countries, but few have been conducted in the United States. This ecological study aims to identify high-risk clusters of suicide in Ohio and assess area level correlates of these clusters. We estimated spatially smoothed standardized mortality ratios (SMR) using Bayesian conditional autoregressive models (CAR) for the period 2004 to 2013. Spatial and spatio-temporal scan statistics were used to detect high-risk clusters of suicide at the census tract level (N=2952). Logistic regression models were used to examine the association between area level correlates and suicide clusters. Nine statistically significant (p<0.05) high-risk spatial clusters and two space-time clusters were identified. We also identified several significant spatial clusters by method of suicide. The risk of suicide was up to 2.1 times higher in high-risk clusters than in areas outside of the clusters (relative risks ranged from 1.22 to 2.14 (p<0.01)). In the multivariate model, factors strongly associated with area suicide rates were socio-economic deprivation and lower provider densities. Efforts to reduce poverty and improve access to health and mental health medical services on the community level represent potentially important suicide prevention strategies.

Spatio-temporal neighborhood impacts on internalizing and externalizing behaviors in U.S. elementary school children: Effect modification by child and family socio-demographics.

Increasing evidence from experimental and non-experimental research has shown that children residing in disadvantaged neighborhoods exhibit greater levels of internalizing and externalizing behaviors, above and beyond individual characteristics, and family or school contexts. Using the ECLS-K, a nationally representative, longitudinal survey of U.S. school children, this study examined direct neighborhood effects on internalizing (N = 14,870; N = 10,610) and externalizing (N = 14,960; N = 10,730) behaviors at the beginning and end of elementary school. Using IPTW propensity scores to mitigate selection bias and ordinary least squares regression, we examined direct neighborhood effects when children were 7 years old (1st grade) and when they were 11 years old (5th grade). We also examined neighborhood effect modification by child child race/ethnicity, sex, family structure, and family SES. Both the direct effect and effect modification models indicated that living in disadvantaged neighborhoods had no impact on either internalizing or externalizing behaviors at the beginning of elementary school (age 7). At the end of elementary school (age 11), we found small, yet significant direct neighborhood associations with effect sizes ranging from 0.12 to 0.18 standard deviations. The effect modification analysis revealed that being black (relative to white) was the strongest moderator of the relationship between neighborhood context and internalizing and externalizing behaviors in 5th grade, with effect sizes ranging from 0.27 to 0.59 standard deviations. Being Hispanic in high poverty neighborhoods was found to be protective for externalizing behaviors, suggesting the presence of the Hispanic health paradox. We also found, that in some neighborhood contexts, child sex, family structure, and family socioeconomic status amplified or dampened the effect of neighborhood, but only for externalizing behaviors. These results demonstrate the importance of age-dependent neighborhood effects and that children with different demographic profiles respond differently to the social contexts in which they are exposed.

Protocol for a mixed-methods study of supplemental oxygen in pulmonary fibrosis.

BACKGROUND: Little is known about whether or how supplemental oxygen affects patients with pulmonary fibrosis. METHODS/DESIGN: A mixed-methods study is described. Patients with pulmonary fibrosis, informal caregivers of pulmonary fibrosis patients and practitioners who prescribe supplemental oxygen will be interviewed to gather data on perceptions of how supplemental oxygen impacts patients. In addition, three hundred pulmonary fibrosis patients who do not use daytime supplemental oxygen will be recruited to participate in a longitudinal, pre-/post- study in which patient-reported outcome (PRO) and activity data will be collected at baseline, immediately before daytime supplemental oxygen is initiated, and then once and again 9-12 months later. Activity data will be collected using accelerometers and portable GPS data recorders. The primary outcome is change in dyspnea from before to one month after supplemental oxygen is initiated. Secondary outcomes include scores from PROs to assess cough, fatigue and quality of life as well as the activity data. In exploratory analyses, we will use longitudinal data analytic techniques to assess the trajectories of outcomes over time while controlling for potentially influential variables. DISCUSSION: Throughout the study and at its completion, results will be posted on the website for our research program (the Participation Program for Pulmonary Fibrosis or P3F) at http://www.pulmonaryfibrosisresearch.org.

Multiple cluster analysis for the identification of high-risk census tracts for out-of-hospital cardiac arrest (OHCA) in Denver, Colorado.

BACKGROUND: Prior research has shown that high-risk census tracts for out-of-hospital cardiac arrest (OHCA) can be identified. High-risk neighborhoods are defined as having a high incidence of OHCA and a low prevalence of bystander cardiopulmonary resuscitation (CPR). However, there is no consensus regarding the process for identifying high-risk neighborhoods. OBJECTIVE: We propose a novel summary approach to identify high-risk neighborhoods through three separate spatial analysis methods: Empirical Bayes (EB), Local Moran's I (LISA), and Getis Ord Gi* (Gi*) in Denver, Colorado. METHODS: We conducted a secondary analysis of prospectively collected Emergency Medical Services data of OHCA from January 1, 2009 to December 31, 2011 from the City and County of Denver, Colorado. OHCA incidents were restricted to those of cardiac etiology in adults ≥18 years. The OHCA incident locations were geocoded using Centrus. EB smoothed incidence rates were calculated for OHCA using Geoda and LISA and Gi* calculated using ArcGIS 10. RESULTS: A total of 1102 arrests in 142 census tracts occurred during the study period, with 887 arrests included in the final sample. Maps of clusters of high OHCA incidence were overlaid with maps identifying census tracts in the below the Denver County mean for bystander CPR prevalence. Five census tracts identified were designated as Tier 1 high-risk tracts, while an additional 7 census tracts where designated as Tier 2 high-risk tracts. CONCLUSION: This is the first study to use these three spatial cluster analysis methods for the detection of high-risk census tracts. These census tracts are possible sites for targeted community-based interventions to improve both cardiovascular health education and CPR training.

Association of neighborhood characteristics with bystander-initiated CPR.

BACKGROUND: For persons who have an out-of-hospital cardiac arrest, the probability of receiving bystander-initiated cardiopulmonary resuscitation (CPR) may be influenced by neighborhood characteristics. METHODS: We analyzed surveillance data prospectively submitted from 29 U.S. sites to the Cardiac Arrest Registry to Enhance Survival between October 1, 2005, and December 31, 2009. The neighborhood in which each cardiac arrest occurred was determined from census-tract data. We classified neighborhoods as high-income or low-income on the basis of a median household income threshold of $40,000 and as white or black if more than 80% of the census tract was predominantly of one race. Neighborhoods without a predominant racial composition were classified as integrated. We analyzed the relationship between the median income and racial composition of a neighborhood and the performance of bystander-initiated CPR. RESULTS: Among 14,225 patients with cardiac arrest, bystander-initiated CPR was provided to 4068 (28.6%). As compared with patients who had a cardiac arrest in high-income white neighborhoods, those in low-income black neighborhoods were less likely to receive bystander-initiated CPR (odds ratio, 0.49; 95% confidence interval [CI], 0.41 to 0.58). The same was true of patients with cardiac arrest in neighborhoods characterized as low-income white (odds ratio, 0.65; 95% CI, 0.51 to 0.82), low-income integrated (odds ratio, 0.62; 95% CI, 0.56 to 0.70), and high-income black (odds ratio, 0.77; 95% CI, 0.68 to 0.86). The odds ratio for bystander-initiated CPR in high-income integrated neighborhoods (1.03; 95% CI, 0.64 to 1.65) was similar to that for high-income white neighborhoods. CONCLUSIONS: In a large cohort study, we found that patients who had an out-of-hospital cardiac arrest in low-income black neighborhoods were less likely to receive bystander-initiated CPR than those in high-income white neighborhoods. (Funded by the Centers for Disease Control and Prevention and others.).

Integration of Spatial and Social Network Analysis in Disease Transmission Studies.

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

The role of vaccine coverage within social networks in cholera vaccine efficacy.

BACKGROUND: Traditional vaccine trial methods have an underlying assumption that the effect of a vaccine is the same throughout the trial area. There are, however, many spatial and behavioral factors that alter the rates of contact among infectious and susceptible individuals and result in different efficacies across a population. We reanalyzed data from a field trial in Bangladesh to ascertain whether there is evidence of indirect protection from cholera vaccines when vaccination rates are high in an individual's social network. METHODS: We analyzed the first year of surveillance data from a placebo-controlled trial of B subunit-killed whole-cell and killed whole-cell-only oral cholera vaccines in children and adult women in Bangladesh. We calculated whether there was an inverse trend for the relation between the level of vaccine coverage in an individual's social network and the incidence of cholera in individual vaccine recipients or placebo recipients after controlling for potential confounding variables. RESULTS: Using bari-level social network ties, we found incidence rates of cholera among placebo recipients were inversely related to levels of vaccine coverage (5.28 cases per 1000 in the lowest quintile vs 3.27 cases per 1000 in the highest quintile; p = 0.037 for trend). Receipt of vaccine by an individual and the level of vaccine coverage of the individual's social network were independently related to a reduced risk of cholera. CONCLUSIONS: Findings indicate that progressively higher levels of vaccine coverage in bari-level social networks can lead to increasing levels of indirect protection of non-vaccinated individuals and could also lead to progressively higher levels of total protection of vaccine recipients.

Socioeconomic context and gastroschisis: exploring associations at various geographic scales.

This study examines associations between area-level socioeconomic factors and the birth defect gastroschisis in order to further our understanding of the etiology of this condition. Specifically, this study explores how measuring socioeconomic conditions at different geographic scales affect the results of statistical models. A population-based case-control study of resident live births was conducted using data from the North Carolina Birth Defect Monitoring Program and the North Carolina composite linked birth files from 1998 through 2004. Neighborhood conditions potentially related to gastroschisis (poverty, unemployment, education, and racial composition) were measured using Census 2000 data and aggregated to several geographic scales. The Brown-Forsythe test of homogeneity of variance was used to select the neighborhood size by examining the effect of neighborhood size on variation in gastroschisis rates. To examine our assumptions about neighborhood size and neighborhood effects on gastroschisis, we estimated a series of logistic regression and multilevel logistic regression models. The Brown-Forsythe test suggested an optimal neighborhood size with a circular radius of approximately 2500 m, which was supported by the statistical analysis. Results indicate a weak association between living in a neighborhood characterized by high poverty and unemployment and an elevated risk of a gastroschisis-affected pregnancy after adjusting for individual-level risk factors. Cross-level interactions indicate that women in low poverty neighborhoods who do not rely on Medicaid have a significantly lower risk of gastroschisis. The choice of neighborhood scale influences model results suggesting that socioeconomic processes may influence health outcomes variably at different scales.

Tracing drinking water to its source: An ecological study of the relationship between textile mills and gastroschisis in North Carolina.

Gastroschisis is a rare birth defect that has increased in prevalence over the past several decades but the etiology of the disease is largely unknown. Using data from the North Carolina Birth Defects Monitoring Program, we estimated multilevel logistic regression models to evaluate the association between drinking water source and upstream textile mills and the risk of a gastroschisis birth. Results indicate that while prenatal exposure to upstream textile mill effluent does not have an impact on the risk of a gastroschisis birth, women relying on public water systems that draw from a surface water source have an elevated risk. These findings suggest the possibility of a contaminant found in higher levels in surface water compared to groundwater.

Spatial and environmental connectivity analysis in a cholera vaccine trial.

This paper develops theory and methods for vaccine trials that utilize spatial and environmental information. Satellite imagery is used to identify whether households are connected to one another via water bodies in a study area in rural Bangladesh. Then relationships between neighborhood-level cholera vaccine coverage and placebo incidence and neighborhood-level spatial variables are measured. The study hypothesis is that unvaccinated people who are environmentally connected to people who have been vaccinated will be at lower risk compared to unvaccinated people who are environmentally connected to people who have not been vaccinated. We use four datasets including: a cholera vaccine trial database, a longitudinal demographic database of the rural population from which the vaccine trial participants were selected, a household-level geographic information system (GIS) database of the same study area, and high resolution Quickbird satellite imagery. An environmental connectivity metric was constructed by integrating the satellite imagery with the vaccine and demographic databases linked with GIS. The results show that there is a relationship between neighborhood rates of cholera vaccination and placebo incidence. Thus, people are indirectly protected when more people in their environmentally connected neighborhood are vaccinated. This result is similar to our previous work that used a simpler Euclidean distance neighborhood to measure neighborhood vaccine coverage [Ali, M., Emch, M., von Seidlein, L., Yunus, M., Sack, D. A., Holmgren, J., et al. (2005). Herd immunity conferred by killed oral cholera vaccines in Bangladesh. Lancet, 366(9479), 44-49]. Our new method of measuring environmental connectivity is more precise since it takes into account the transmission mode of cholera and therefore this study validates our assertion that the oral cholera vaccine provides indirect protection in addition to direct protection.