Thomas O. Talbot

Positional error in automated geocoding of residential addresses

BackgroundPublic health applications using geographic information system (GIS) technology are steadily increasing. Many of these rely on the ability to locate where people live with respect to areas of exposure from environmental contaminants. Automated geocoding is a method used to assign geographic coordinates to an individual based on their street address. This method often relies on street centerline files as a geographic reference. Such a process introduces positional error in the geocoded point. Our study evaluated the positional error caused during automated geocoding of residential addresses and how this error varies between population densities. We also evaluated an alternative method of geocoding using residential property parcel data.ResultsPositional error was determined for 3,000 residential addresses using the distance between each geocoded point and its true location as determined with aerial imagery. Error was found to increase as population density decreased. In rural areas of an upstate New York study area, 95 percent of the addresses geocoded to within 2,872 m of their true location. Suburban areas revealed less error where 95 percent of the addresses geocoded to within 421 m. Urban areas demonstrated the least error where 95 percent of the addresses geocoded to within 152 m of their true location. As an alternative to using street centerline files for geocoding, we used residential property parcel points to locate the addresses. In the rural areas, 95 percent of the parcel points were within 195 m of the true location. In suburban areas, this distance was 39 m while in urban areas 95 percent of the parcel points were within 21 m of the true location.ConclusionResearchers need to determine if the level of error caused by a chosen method of geocoding may affect the results of their project. As an alternative method, property data can be used for geocoding addresses if the error caused by traditional methods is found to be unacceptable.

Evaluation of spatial filters to create smoothed maps of health data

Spatial filters have been used as an easy and intuitive way to create smoothed disease maps. Birth weight data from New York State for 1994 and 1995 are used to compare the traditional filter type of fixed geographical size with a filter size of constant or nearly constant population size. The latter are more appropriate for mapping disease in geographic areas with widely varying population density, such as New York State. Issues such as the choice of population size for the filter, the scale of smoothing, the ability to detect true spatial variation and the ability to smooth over random spatial noise are evaluated and discussed.

Surveillance of the short-term impact of fine particle air pollution on cardiovascular disease hospitalizations in New York State.

BackgroundStudies have shown that the effects of particulate matter on health vary based on factors including the vulnerability of the population, health care practices, exposure factors, and the pollutant mix.MethodsWe used time-stratified case-crossover to estimate differences in the short-term impacts of PM2.5 on cardiovascular disease hospital admissions in New York State by geographic area, year, age, gender, co-morbid conditions, and area poverty rates.ResultsPM2.5 had a stronger impact on heart failure than other cardiovascular diagnoses, with 3.1% of heart failure admissions attributable to short-term PM2.5 exposure over background levels of 5 ug/m3. Older adults were significantly more susceptible to heart failure after short-term ambient PM2.5 exposure than younger adults.ConclusionThe short-term impact of PM2.5 on cardiovascular disease admissions, and modifications of that impact, are small and difficult to measure with precision. Multi-state collaborations will be necessary to attain more precision to describe spatiotemporal differences in health impacts.

Seasonality and trend in blood lead levels of New York State children

BackgroundEnvironmental exposure to lead remains a significant health problem for children. The costs of lead exposure in children are estimated to be considerably more than other childhood diseases of environmental origin. While long-term trends in blood lead levels (BLLs) among children are declining, seasonal variation persists. Cross-sectional studies have found a peak in summer months. Part of this variation may be due to increased exposure to lead paint on window sills and through increased contact with soils containing lead during the summer. The current study represents the largest published population-based study on seasonality and trends in the BLLs of children to date. In addition, the results offer a comparison of recent data on seasonality of BLLs in New York State children, to studies conducted over the past three decades.Methods262,687 New York State children born between 1994 and 1997 were screened for blood lead within 2 weeks of their first or second birthdays. Time series analyses of blood lead data from these children were conducted to study the seasonality and trends of BLLs.ResultsChildrens blood lead values showed a distinct seasonal cycle on top of a long-term decreasing trend. The geometric mean BLL declined by about 24% for children born between 1994 and 1997. The prevalence of elevated BLLs in two-year-olds was almost twice that in one-year-olds over the time period. Nearly twice as many children had elevated BLLs in the late summer compared to late winter/early spring. In this and previous cross-sectional studies, the amount of seasonality as a proportion of the mean ranged between 15% and 30%.ConclusionPediatricians should be aware of the seasonality of BLLs. For example, if a two-year-old receives a borderline result during the winter, it is possible that the levels would have been higher if he had been tested during the summer. However, physicians should continue to screen children at their normally scheduled well-child visits rather than delaying until summertime and possibly postponing the discovery of an elevated BLL. Age, season, and time trends still need to be considered in lead studies and result interpretation.

Geographic Analysis of Blood Lead Levels in New York State Children Born 1994–1997

We examined the geographic distribution of the blood lead levels (BLLs) of 677,112 children born between 1994 and 1997 in New York State and screened before 2 years of age. Five percent of the children screened had BLLs higher than the current Centers for Disease Control and Prevention action level of 10 μg/dL. Rates were higher in upstate cities than in the New York City area. We modeled the relationship between BLLs and housing and socioeconomic characteristics at the ZIP code level. Older housing stock, a lower proportion of high school graduates, and a higher percentage of births to African-American mothers were the community characteristics most associated with elevated BLLs. Although the prevalence of children with elevated BLLs declined 44% between those born in 1994 and those born in 1997, the rate of improvement may be slowing down. Lead remains an environmental health problem in inner-city neighborhoods, particularly in upstate New York. We identified areas having a high prevalence of children with elevated BLLs. These communities can be targeted for educational and remediation programs. The model locates areas with a higher or lower prevalence of elevated BLLs than expected. These communities can be studied further at the individual level to better characterize the factors that contribute to these differences.

Data quality and the spatial analysis of disease rates: congenital malformations in New York State

Spatial analyses of disease rates are increasing as the hardware and software used in disease surveillance and cluster investigations become more accessible and easier to use. The results of these analyses should be interpreted with caution since inconsistencies in health outcome reporting and population estimates may lead to erroneous conclusions. In this report we provide an example, using data on congenital malformations in New York State, to show how under-reporting of malformations by some New York City hospitals can lead to apparent clusters of malformations in other areas of the state where reporting is more complete. We illustrate how spatial analysis techniques can be used to locate under-reporting problems and determine the extent to which the problem exists.

Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates.

An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM2.5 in areas with and without air quality monitors by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM2.5 concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM2.5 concentrations from satellite data can be used to supplement PM2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.

Developing consistent data and methods to measure the public health impacts of ambient air quality for Environmental Public Health Tracking: progress to date and future directions.

Environmental Public Health Tracking (EPHT) staff at the state and national levels are developing nationally consistent data and methods to estimate the impact of ozone and fine particulate matter on hospitalizations for asthma and myocardial infarction. Pilot projects have demonstrated the feasibility of pooling state hospitalization data and linking these data to The United States Environmental Protection Agency (EPA) statistically based ambient air estimates for ozone and fine particulates. Tools were developed to perform case-crossover analyses to estimate concentration–response (C-R) functions. A weakness of analyzing one state at a time is that the effects are relatively small compared to their confidence intervals. The EPHT program will explore ways to statistically combine the results of peer-reviewed analyses from across the country to provide more robust C-R functions and health impact estimates at the local level. One challenge will be to routinely share data for these types of analyses at fine geographic and temporal scales without disclosing confidential information. Another challenge will be to develop C-R estimates which take into account time, space, or other relevant effect modifiers.

Development of an interactive environmental public health tracking system for data analysis, visualization, and reporting.

Healthcare providers and governmental agencies routinely collect and report data on health outcomes. In addition, governmental agencies and industry collect and report information on environmental hazards and exposures that may impact health. Use of these data for environmental public health tracking has been a challenge because these data are managed by different data stewards, may contain confidential information that must be protected, and have not been collected in a manner to facilitate linkages. Available tools for analysis, visualization, and reporting of these data are either difficult to use or not available through a common user interface. The New York State Department of Health has developed a user-friendly interactive system to access and link these data while protecting confidential information. The Environmental Public health tracking system provides tools for describing the geographic patterns, trends, and statistical associations between health, environmental exposure, and environmental hazard data. These tools provide descriptive statistics and automated techniques that smooth the data in order to protect patient confidentiality and reduce random fluctuations in rates due to small numbers. This article describes the user interface, data linkages, and analytic, visualization, and reporting tools.

Public domain small-area cancer incidence data for New York State, 2005-2009

There has long been a demand for cancer incidence data at a fine geographic resolution for use in etiologic hypothesis generation and testing, methodological evaluation and teaching. In this paper we describe a public domain dataset containing data for 23 anatomic sites of cancer diagnosed in New York State, USA between 2005 and 2009 at the census block group level. The dataset includes 524,503 tumours distributed across 13,823 block groups with an average population of about 1400. In addition, the data have been linked with race/ethnicity and with socioeconomic indicators such as income, educational attainment and language proficiency. We demonstrate the application of the dataset by confirming two well-established relationships: that between breast cancer and median household income and that between stomach cancer and Asian race. We foresee that this dataset will serve as the basis for a wide range of spatial analyses and as a benchmark for evaluating spatial methods in the future.