The spatial relationship between infant mortality and birth defect rates in a U.S. city

Giardiasis notifications are associated with socioeconomic status in Sydney, Australia: a spatial analysis

OBJECTIVE

In developed countries prolonged symptoms due to, or following, Giardia intestinalis infection can have a significant impact on the quality of life. In this research, we investigate the presence of a socioeconomic status (SES) gradient in the reporting of giardiasis in South West Sydney Local Health District (SWSLHD), New South Wales (NSW), Australia, across geographic scales.

METHODS

We used a large database, spatial-cluster analysis and a linear model.

RESULTS

Firstly, we found one spatial cluster of giardiasis in one of the most advantaged neighbourhoods of SWSLHD. Secondly, rates of giardiasis notifications were significantly and consistently lower in SWSLHD compared to an unnamed advantaged Local Health District and NSW over multiple years. Finally, we found an overall significant positive dose-response relationship between counts of giardiasis and area-level SES.

CONCLUSIONS

Lower reporting in disadvantaged areas may represent true differences in incidence across SES groups or may result from differential use of health services and reporting. Implications for public health: If the disparities result from differential use of health services, research should be directed toward identifying barriers and facilitators of use. If disparities result from a true difference in incidence, then the behavioural mediators between SES and giardiasis should be identified and addressed.

Association of maternal and community factors with enrollment in home visiting among at-risk, first-time mothers

OBJECTIVES

We identified individual and contextual factors associated with referral and enrollment in home visiting among at-risk, first-time mothers.

METHODS

We retrospectively studied referral and enrollment in a regional home visiting program from 2007 to 2009 in Hamilton County, Ohio. Using linked vital statistics and census tract data, we obtained individual and community measures on first-time mothers meeting eligibility criteria for home visiting (low income, unmarried, or age < 18 years). Generalized linear modeling was performed to determine factors associated with relative risk (RR) of (1) referral to home visiting among eligible mothers and (2) enrollment after referral.

RESULTS

Of 8187 first-time mothers eligible for home visiting, 2775 were referred and 1543 were enrolled. Among referred women, high school completion (RR = 1.10) and any college (RR = 1.17) compared with no high school completion were associated with increased enrollment, and enrollment was less likely for those living in communities with higher socioeconomic deprivation (RR = 0.71; P < .05).

CONCLUSIONS

Barriers to enrollment in home visiting persisted at multiple ecological levels. Ongoing evaluation of enrollment in at-risk populations is critical as home visiting programs are implemented and expanded.

Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects

Background: Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots. Conclusions: RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.

Association of vitamin D status with socio-demographic factors in Calgary, Alberta: an ecological study using Census Canada data

BACKGROUND

Low 25-hydroxyvitamin D levels are a global health problem with northern countries such as Canada at particular risk. A number of sociodemographic factors have been reported to be associated with low vitamin D levels but prior studies have been limited by the ability of the researchers to gather this data directly from clinical trial participants. The purpose of this study was to use a novel methodology of inferring sociodemographic variables to evaluate the correlates of vitamin D levels in individuals dwelling in the City of Calgary, Alberta, Canada.

METHODS

We utilized data on vitamin D test results from Calgary Laboratory Services between January 1 2010 and August 31 2011. In addition to vitamin D level, we recorded age, sex, and vitamin D testing month as individual-level variables. We inferred sociodemographic variables by associating results with census dissemination areas and using Census Canada data to determine immigration status, education, median household income and first nations status as clustered variables. Associations between vitamin D status and the individual- and dissemination area-specific variables were examined using the population-averaged regression model by a generalized estimating equations approach to account for the clustering in the data.

RESULTS

158,327 individuals were included. Age, sex, month of vitamin D testing (at an individual level), and education, immigrant status, first nations status and income (at an aggregate level) were all statistically significant predictors of vitamin D status.

CONCLUSIONS

Vitamin D status was associated with a number of sociodemographic variables. Knowledge of these variables may improve targeted education and public health initiatives.

Evaluating patterns of a white-band disease (WBD) outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering

Background

Despite being one of the first documented, there is little known of the causative agent or environmental stressors that promote white-band disease (WBD), a major disease of Caribbean Acropora palmata. Likewise, there is little known about the spatiality of outbreaks. We examined the spatial patterns of WBD during a 2004 outbreak at Buck Island Reef National Monument in the US Virgin Islands.

Methodology/Principal Findings

Ripley's K statistic was used to measure spatial dependence of WBD across scales. Localized clusters of WBD were identified using the DMAP spatial filtering technique. Statistics were calculated for colony- (number of A. palmata colonies with and without WBD within each transect) and transect-level (presence/absence of WBD within transects) data to evaluate differences in spatial patterns at each resolution of coral sampling. The Ripley's K plots suggest WBD does cluster within the study area, and approached statistical significance (p = 0.1) at spatial scales of 1100 m or less. Comparisons of DMAP results suggest the transect-level overestimated the prevalence and spatial extent of the outbreak. In contrast, more realistic prevalence estimates and spatial patterns were found by weighting each transect by the number of individual A. palmata colonies with and without WBD.

Conclusions

As the search for causation continues, surveillance and proper documentation of the spatial patterns may inform etiology, and at the same time assist reef managers in allocating resources to tracking the disease. Our results indicate that the spatial scale of data collected can drastically affect the calculation of prevalence and spatial distribution of WBD outbreaks. Specifically, we illustrate that higher resolution sampling resulted in more realistic disease estimates. This should assist in selecting appropriate sampling designs for future outbreak investigations. The spatial techniques used here can be used to facilitate other coral disease studies, as well as, improve reef conservation and management.

Small area estimation of sparse disease counts using shared component models-application to birth defect registry data in New South Wales, Australia

In the field of disease mapping, little has been done to address the issue of analysing sparse health datasets. We hypothesised that by modelling two outcomes simultaneously, one would be able to better estimate the outcome with a sparse count. We tested this hypothesis utilising Bayesian models, studying both birth defects and caesarean sections using data from two large, linked birth registries in New South Wales from 1990 to 2004. We compared four spatial models across seven birth defects: spina bifida, ventricular septal defect, OS atrial septal defect, patent ductus arteriosus, cleft lip and or palate, trisomy 21 and hypospadias. For three of the birth defects, the shared component model with a zero-inflated Poisson (ZIP) extension performed better than other simpler models, having a lower deviance information criteria (DIC). With spina bifida, the ratio of relative risk associated with the shared component was 2.82 (95% CI: 1.46-5.67). We found that shared component models are potentially beneficial, but only if there is a reasonably strong spatial correlation in effect for the study and referent outcomes.

A scan statistic for continuous data based on the normal probability model

Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.

Spatial analysis of notified cryptosporidiosis infections in Brisbane, Australia

PURPOSE

This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia.

METHODS

We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis.

RESULTS

Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%.

CONCLUSIONS

There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales

BACKGROUND

Long-term human exposure to ambient pollutants can be an important contributing or etiologic factor of many chronic diseases. Spatiotemporal estimation (mapping) of long-term exposure at residential areas based on field observations recorded in the U.S. Environmental Protection Agency's Air Quality System often suffer from missing data issues due to the scarce monitoring network across space and the inconsistent recording periods at different monitors.

OBJECTIVE

We developed and compared two upscaling methods: UM1 (data aggregation followed by exposure estimation) and UM2 (exposure estimation followed by data aggregation) for the long-term PM10 (particulate matter with aerodynamic diameter < or = 10 microm) and ozone exposure estimations and applied them in multiple time scales to estimate PM and ozone exposures for the residential areas of the Health Effects of Air Pollution on Lupus (HEAPL) study.

METHOD

We used Bayesian maximum entropy (BME) analysis for the two upscaling methods. We performed spatiotemporal cross-validations at multiple time scales by UM1 and UM2 to assess the estimation accuracy across space and time.

RESULTS

Compared with the kriging method, the integration of soft information by the BME method can effectively increase the estimation accuracy for both pollutants. The spatiotemporal distributions of estimation errors from UM1 and UM2 were similar. The cross-validation results indicated that UM2 is generally better than UM1 in exposure estimations at multiple time scales in terms of predictive accuracy and lack of bias. For yearly PM10 estimations, both approaches have comparable performance, but the implementation of UM1 is associated with much lower computation burden.

CONCLUSION

BME-based upscaling methods UM1 and UM2 can assimilate core and site-specific knowledge bases of different formats for long-term exposure estimation. This study shows that UM1 can perform reasonably well when the aggregation process does not alter the spatiotemporal structure of the original data set; otherwise, UM2 is preferable.

Role of geographic information systems in birth defects surveillance and research

BACKGROUND

With the significant advancement of geographic information systems (GIS), mapping and evaluating the spatial distribution of health events has become easier. We examine the role of GIS in birth defects surveillance and research.

METHODS

We briefly describe the geocoding process and potential problems in accuracy of the obtained geocodes, and some of the capabilities and limitations of GIS. We illustrate how GIS has been applied using the Metropolitan Atlanta Congenital Defects Program geocoded dataset. We provide some comments on potential data quality and confidentiality issues with birth defects in relation to GIS.

RESULTS

It is desirable to geocode addresses using a multistrategy approach to achieve a high-quality and accurate GIS dataset. Beyond the basic but important function of mapping, sophisticated statistical approaches and software are available to analyze the spatial or spatial-temporal occurrence of birth defects, alone or in association with environmental hazards, and to present this information without compromising the confidentiality of the subjects.

CONCLUSIONS

We recommend a broad and systematic use of GIS in birth defects spatial surveillance and research.

Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public

BACKGROUND

To communicate population-based cancer statistics, cancer researchers have a long tradition of presenting data in a spatial representation, or map. Historically, health data were presented in printed atlases in which the map producer selected the content and format. The availability of geographic information systems (GIS) with comprehensive mapping and spatial analysis capability for desktop and Internet mapping has greatly expanded the number of producers and consumers of health maps, including policymakers and the public.Because health maps, particularly ones that show elevated cancer rates, historically have raised public concerns, it is essential that these maps be designed to be accurate, clear, and interpretable for the broad range of users who may view them. This article focuses on designing maps to communicate effectively. It is based on years of research into the use of health maps for communicating among public health researchers.

RESULTS

The basics for designing maps that communicate effectively are similar to the basics for any mode of communication. Tasks include deciding on the purpose, knowing the audience and its characteristics, choosing a media suitable for both the purpose and the audience, and finally testing the map design to ensure that it suits the purpose with the intended audience, and communicates accurately and effectively. Special considerations for health maps include ensuring confidentiality and reflecting the uncertainty of small area statistics. Statistical maps need to be based on sound practices and principles developed by the statistical and cartographic communities.

CONCLUSION

The biggest challenge is to ensure that maps of health statistics inform without misinforming. Advances in the sciences of cartography, statistics, and visualization of spatial data are constantly expanding the toolkit available to mapmakers to meet this challenge. Asking potential users to answer questions or to talk about what they see is still the best way to evaluate the effectiveness of a specific map design.

Spatial epidemiology: current approaches and future challenges

Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.

Spatial analysis of disease--applications

The application of spatial statistical analysis to health data has reached adolescence. The theory and the software are both still maturing. We are drawing upon the experiences of the geostatisticians in modeling surfaces and the econometricians in modeling time series. "New and improved" computer algorithms are constantly being provided to implement the evolving theory or to improve the processing in terms of stability, reliability, and efficiency. We will come of age when we have the theory, the software, and the process to reliably produce "generalized spatio-temporal" models suitable for health data. In the meantime, biostatisticians need to acknowledge when their data is not independently distributed and to consider the spatial correlation in their analysis. This chapter provided examples using four available methods. The methods were spatial filtering, identifying clusters using the spatial scan statistic, hierarchical modeling, and conditional autoregression modeling.

Geocoding addresses from a large population-based study: lessons learned

BACKGROUND

Geographic information systems (GIS) and spatial statistics are useful for exploring the relation between geographic location and health. The ultimate usefulness of GIS depends on both completeness and accuracy of geocoding (the process of assigning study participants' residences latitude/longitude coordinates that closely approximate their true locations, also known as address matching). The goal of this project was to develop an iterative geocoding process that would achieve a high match rate in a large population-based health study.

METHODS

Data were from a study conducted in Wisconsin using mailing addresses of participants who were interviewed by telephone from 1988 to 1995. We standardized the addresses according to US Postal Service guidelines, used desktop GIS geocoding software and two versions of the Topologically Integrated Geographic Encoding and Referencing street maps, accessed Internet mapping engines for problematic addresses, and recontacted a small number of study participants' households. We also tabulated the project's cost, time commitment, software requirements, and brief notes for each step and their alternatives.

RESULTS

Of the 14,804 participants, 97% were ultimately assigned latitude/longitude coordinates corresponding to their respective residences. The remaining 3% were geocoded to their zip code centroid.

CONCLUSION

The multiple methods described in this work provide practical information for investigators who are considering the use of GIS in their population health research.

Breast cancer surveillance using gridded population units, Connecticut, 1992 to 1995

PURPOSE

To assess geographic variation in invasive breast cancer across Connecticut using gridded population areas to enumerate cases and the population at-risk.

METHODS

The state's land mass was divided into 5168, 1-by-1 square mile areas and the population of women, 20+ years of age, within each location was estimated by areal interpolation of the 1990 US Census Block Group STF-3A data file. Using information on breast cancer incidence, 1992 to 1995, from the Connecticut Tumor Registry, latitude-longitude coordinates for place of residence at the time of breast cancer diagnosis were determined for 8530 records and assigned to appropriate grid locations. A spatial scan statistic was used to detect variation in incidence and test the significance of observed differences across the state. Standardized Incidence Ratios (SIRs) described the proportional change in the age-adjusted breast cancer incidence rate across gridded locations.

RESULTS

The statewide age-adjusted invasive cancer incidence rate was 163.6/100,000 women/year. The spatial scan statistic identified three locations around Connecticut with significantly low incidence rates and four places where rates were significantly high. The most probable place of low incidence was rural Northeastern Connecticut where risk of disease, relative to elsewhere around the state, was 0.73 (p = 0.001). The most probable location of elevated incidence was a suburban location in Southwestern Connecticut with a relative risk of 2.02 (p = 0.001).

CONCLUSIONS

Visual representation of disease incidence and underlying populations at-risk according to gridded units provides a useful tool for assessing small area variation in disease patterns.

Development of a questionnaire for quantitative assessment in the field of health and human rights

We hypothesize that a human rights framework would be able to analyse central health-related societal issues within important settings like the work place, the family or the health care system. Our study goal was the development and population-based evaluation of a questionnaire for assessment of the perceived human rights status. A questionnaire (HR-14) was developed from the guiding principles of international human rights legislation. For its psychometric evaluation, computer-assisted telephone interviews were conducted in four cities in Europe (Munich, Dresden, Vienna and Bern). Cronbach's alpha for internal consistency was 0.76. Factor analysis supported the concept of human rights as indivisible and interdependent. Extracted factors were consistent with the preliminary settings of family and friends, health care system and community at large, and a supplementary setting workplace. Perceived human rights status was associated with physical function, mental/emotional health, age, study region, general health and employment status. We conclude that it is possible to develop a human rights questionnaire with good psychometric properties. Measurement of the perceived human rights status of populations and population groups may contribute to health policies sensitive to human rights.

Spatial statistical methods in health

The study of the geographical distribution of disease incidence and its relationship to potential risk factors (referred to here as "geographical epidemiology") has provided, and continues to provide, rich ground for the application and development of statistical methods and models. In recent years increasingly powerful and versatile statistical tools have been developed in this application area. This paper discusses the general classes of problem in geographical epidemiology and reviews the key statistical methods now being employed in each of the application areas identified. The paper does not attempt to exhaustively cover all possible methods and models, but extensive references are provided to further details and to additional approaches. The overall aim is to provide a picture of the "current state of the art" in the use of spatial statistical methods in epidemiological and public health research. Following the review of methods, the main software environments which are available to implement such methods are discussed. The paper concludes with some brief general reflections on the epidemiological and public health implications of the use of spatial statistical methods in health and on associated benefits and problems.