The Geography of Diabetes in London, Canada: The Need for Local Level Policy for Prevention and Management

Recent reports aimed at improving diabetes care in socially disadvantaged populations suggest that interventions must be tailored to meet the unique needs of the local community—specifically, the community’s geography. We have examined the spatial distribution of diabetes in the context of socioeconomic determinants of health in London (Ontario, Canada) to characterize neighbourhoods in an effort to target these neighbourhoods for local level community-based program planning and intervention. Multivariate spatial-statistical techniques and geographic information systems were used to examine diabetes rates and socioeconomic variables aggregated at the census tract level. Creation of a deprivation index facilitated investigation across multiple determinants of health. Findings from our research identified ‘at risk’ neighbourhoods in London with socioeconomic disadvantage and high diabetes. Future endeavours must continue to identify local level trends in order to support policy development, resource planning and care for improved health outcomes and improved equity in access to care across geographic regions

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

<p>Abstract</p> <p>Background</p> <p>Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.</p> <p>Methods</p> <p>Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM_2.5concentrations in a dense receptor grid over a 1 km²area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.</p> <p>Results</p> <p>Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM_2.5were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.</p> <p>Conclusions</p> <p>The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.</p

Cardiopulmonary Impact of Particulate Air Pollution in High-Risk Populations: JACC State-of-the-Art Review

Fine particulate air pollution <2.5 μm in diameter (PM(2.5)) is a major environmental threat to global public health. Multiple national and international medical and governmental organizations have recognized PM(2.5) as a risk factor for cardiopulmonary diseases. A growing body of evidence indicates that several personal-level approaches that reduce exposures to PM(2.5) can lead to improvements in health endpoints. Novel and forward-thinking strategies including randomized clinical trials are important to validate key aspects (e.g., feasibility, efficacy, health benefits, risks, burden, costs) of the various protective interventions, in particular among real-world susceptible and vulnerable populations. This paper summarizes the discussions and conclusions from an expert workshop, Reducing the Cardiopulmonary Impact of Particulate Matter Air Pollution in High Risk Populations, held on May 29 to 30, 2019, and convened by the National Institutes of Health, the U.S. Environmental Protection Agency, and the U.S. Centers for Disease Control and Prevention

Ambient PM₂.₅, O₃, and NO₂ Exposures and Associations with Mortality over 16 Years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC)

Background: Few studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up. Objective: We investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM₂.₅), ozone (O₃), and nitrogen dioxide (NO₂) in a national cohort of about 2.5 million Canadians. Methods: We assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects’ residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m³ for PM₂.₅, 9.5 ppb for O₃, and 8.1 ppb for NO₂. Results: PM₂.₅, O₃, and NO₂ were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM₂.₅ alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM₂.₅ and O₃, but increased associations with NO₂. Conclusions: In this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM₂.₅, O₃, and NO₂

Simulated control groups and the evaluation of job-creation programs

Tables

Job creation and the economic stimulus package

"Draft.""This paper was prepared for the Brookings Institution and the U.S. Department of Labor, OPER/OPE.

Manganese in teeth and neurodevelopment in young Mexican-American children

IRETIntroduction: Manganese (Mn) is an essential nutrient but higher exposure has been associated with poorer neurodevelopment in children. Methods: We measured Mn levels in prenatal (Mnpre) (n=197) and postnatal (Mnpost) dentin (n=193) from children's shed teeth using laser ablation inductively coupled plasma mass spectroscopy and examined the relationship with children's scores on the Mental Development Index (MDI) and Psychomotor Development Index (PDI) on the Bayley Scales of Infant Development at 6, 12, and 24-months. We explored non-linear associations and interactions by sex, blood lead concentrations and maternal iron status during pregnancy. Results: A two-fold increase of Mnpost levels in dentin was associated with small decreases in MDI at 6-months and 12-months of age. We also observed a non-linear relationship between Mnpost levels and PDI at 6-months. We found effect modification by sex for Mnpost levels and neurodevelopment at 6-months with stronger effects among girls for both MDI (-1.5 points; 95% Confidence Interval (CI): -2.4, -0.6) and PDI (-1.8 points; 95% CI: -3.3, -0.3). Girls whose mothers had lower hemoglobin levels experienced larger decreases in MDI and PDI associated with Mnpre levels than girls whose mothers had higher hemoglobin levels (pinteraction=0.007 and 0.09, respectively). We did not observe interactions with blood lead concentrations or any relationships with neurodevelopment at 24-months. Conclusions: Using Mn measurements in tooth dentin, a novel biomarker that provides prenatal and early postnatal levels, we observed negative transient associations between postnatal Mn levels and early neurodevelopment with effect modification by sex and interactions with prenatal hemoglobinIntroducción: El manganeso (Mn) es un nutriente esencial, pero una mayor exposición se ha asociado con un desarrollo neurológico más deficiente en los niños. Métodos: Medimos los niveles de manganeso en la dentina prenatal (Mnpre) (n=197) y posnatal (Mnpost) (n=193) de los dientes caídos de los niños mediante espectroscopía de masas de plasma acoplado inductivamente por ablación láser y examinamos la relación con las puntuaciones de los niños en el Desarrollo mental. Índice (MDI) e Índice de Desarrollo Psicomotor (PDI) en las Escalas de Desarrollo Infantil de Bayley a los 6, 12 y 24 meses. Exploramos asociaciones e interacciones no lineales por sexo, concentraciones de plomo en sangre y estado de hierro materno durante el embarazo. Resultados: Un aumento del doble de los niveles de Mnpost en la dentina se asoció con pequeñas disminuciones en el MDI a los 6 y 12 meses de edad. También observamos una relación no lineal entre los niveles de Mnpost y PDI a los 6 meses. Encontramos una modificación del efecto por sexo para los niveles de Mnpost y el neurodesarrollo a los 6 meses con efectos más fuertes entre las niñas tanto para MDI (-1.5 puntos; Intervalo de confianza (IC) del 95 %): -2.4, -0.6) como para PDI (-1.8 puntos; 95 puntos). % IC: -3,3, -0,3). Las niñas cuyas madres tenían niveles más bajos de hemoglobina experimentaron mayores disminuciones en MDI y PDI asociadas con los niveles de Mnpre que las niñas cuyas madres tenían niveles más altos de hemoglobina (pinteracción = 0,007 y 0,09, respectivamente). No observamos interacciones con las concentraciones de plomo en sangre ni ninguna relación con el neurodesarrollo a los 24 meses. Conclusiones: Usando mediciones de Mn en dentina dental, un nuevo biomarcador que proporciona niveles prenatales y posnatales tempranos, observamos asociaciones transitorias negativas entre los niveles posnatales de Mn y el neurodesarrollo temprano con modificación del efecto por sexo e interacciones con la hemoglobina prenatalUniversity of California, United StatesIcahn School of Medicine at Mount Sinai, United StatesUniversity of Sydney, AustraliaUniversidad Nacional, Costa RicaInstituto Regional de Estudios en Sustancias Tóxica

PM2.5 and Diabetes and Hypertension Incidence in the Black Women’s Health Study

BackgroundClinical studies have shown that exposure to fine particulate matter (PM2.5) can increase insulin resistance and blood pressure. The epidemiologic evidence for an association of PM2.5 exposure with the incidence of type 2 diabetes or hypertension is inconsistent. Even a modest association would have great public health importance given the ubiquity of exposure and high prevalence of the conditions.MethodsWe used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes and hypertension associated with exposure to PM2.5 in a large cohort of African American women living in 56 metropolitan areas across the US, using data from the Black Women's Health Study. Pollutant levels were estimated at all residential locations over follow-up with a hybrid model incorporating land use regression and Bayesian Maximum Entropy techniques.ResultsDuring 1995 to 2011, 4,387 cases of diabetes and 9,570 cases of hypertension occurred. In models controlling for age, questionnaire cycle, and metro area, there were positive associations with diabetes (HR = 1.13, 95% CI = 1.04, 1.24) and hypertension (HR = 1.06, 95% CI = 1.00, 1.12) per interquartile range of PM2.5 (2.9 μg/m). Multivariable HRs, however, were 0.99 (95% CI = 0.90, 1.09) for diabetes and 0.99 (95% CI = 0.93, 1.06) for hypertension.ConclusionsOur results provide little support for an association of PM2.5 with diabetes or hypertension incidence