Users’ perspectives on epidemiological, GIS and point pattern approaches to analysing environment and health data

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

BACKGROUND

The utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution's protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.

RESULTS

We show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.

CONCLUSION

The results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

Association between Ambient Air Pollution and Asthma Prevalence in Different Population Groups Residing in Eastern Texas, USA

Air pollution has been an on-going research focus due to its detrimental impact on human health. However, its specific effects on asthma prevalence in different age groups, genders and races are not well understood. Thus, the present study was designed to examine the association between selected air pollutants and asthma prevalence in different population groups during 2010 in the eastern part of Texas, USA.The pollutants considered were particulate matter (PM2.5 with an aerodynamic diameter less than 2.5 micrometers) and surface ozone. The population groups were categorized based on age, gender, and race. County-wise asthma hospital discharge data for different age, gender, and racial groups were obtained from Texas Asthma Control Program, Office of Surveillance, Evaluation and Research, Texas Department of State Health Services. The annual means of the air pollutants were obtained from the United States Environmental Protection Agency (U.S. EPA)'s air quality system data mart program. Pearson correlation analyzes were conducted to examine the relationship between the annual mean concentrations of pollutants and asthma discharge rates (ADR) for different age groups, genders, and races. The results reveal that there is no significant association or relationship between ADR and exposure of air pollutants (PM2.5, and O₃). The study results showed a positive correlation between PM2.5 and ADR and a negative correlation between ADR and ozone in most of the cases. These correlations were not statistically significant, and can be better explained by considering the local weather conditions. The research findings facilitate identification of hotspots for controlling the most affected populations from further environmental exposure to air pollution, and for preventing or reducing the health impacts.

Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002

OBJECTIVE

This study aimed to examine whether spatial-temporal patterns of dengue can be used to identify areas at risk of dengue hemorrhagic fever (DHF).

METHODS

Three indices - probability of case-occurrence, mean duration per wave, and transmission intensity - were used to differentiate eight local spatial-temporal patterns of dengue during the 2002 epidemic in Kaohsiung, Taiwan. DHF densities (DHF cases/km(2) per 100 dengue cases) in each spatial-temporal typed area were compared.

RESULTS

Areas with three high indices correlated with the highest DHF density: (1) high transmission intensity only; (2) long duration of wave only, and (3) high transmission intensity plus long duration of wave. However, cumulative incidences of dengue cases were not correlated with DHF densities.

CONCLUSION

Three spatial-temporal indices of dengue could provide useful information to identify areas at high risk of DHF.

Issues of scale and uncertainty in the global remote sensing of disease

Scale and uncertainty are important issues for the global prediction of disease. Disease mapping over the entire surface of the Earth usually involves the use of remotely sensed imagery to provide environmental covariates of disease risk or disease vector density. It further implies that the spatial resolution of such imagery is relatively coarse (e.g., 8 or 1km). Use of a coarse spatial resolution limits the information that can be extracted from imagery and has important effects on the results of epidemiological analyses. This paper discusses geostatistical models for (i) characterizing the scale(s) of spatial variation in data and (ii) changing the scale of measurement of both the data and the geostatistical model. Uncertainty is introduced, highlighting the fact that most epidemiologists are interested in accuracy, aspects of which can be estimated with measurable quantities. This paper emphasizes the distinction between data- and model-based methods of accuracy assessment and gives examples of both. The key problem of validating global maps is considered.

Geographic Information Systems (GIS) for Health Promotion and Public Health: A Review

The purpose of this literature review is to identify how geographic information system (GIS) applications have been used in health-related research and to critically examine the issues, strengths, and challenges inherent to those approaches from the lenses of health promotion and public health. Through the review process, conducted in 2007, it is evident that health promotion and public health applications of GIS can be generally categorized into four predominant themes: disease surveillance (n = 227), risk analysis (n = 189), health access and planning (n = 138), and community health profiling (n = 115). This review explores how GIS approaches have been used to inform decision making and discusses the extent to which GIS can be applied to address health promotion and public health questions. The contribution of this literature review will be to generate a broader understanding of how GIS-related methodological techniques and tools developed in other disciplines can be meaningfully applied to applications in public health policy, promotion, and practice.

Exploratory analysis of potential risk factors of a rare disease: spatial distribution of adrenocortical carcinoma in Israel as a case study

The underlying assumption of the proposed exploratory approach is that, if the geographic patterns of different diseases are compared, the cases of a 'subject' disease should occur closer to cases of a disease with similar environmental risk factors (etiology) and farther away from cases of a disease with different etiology. In the present study, the performance of proposed approach is investigated by cross-examination of the spatial patterns of three widespread cancers--lung, larynx and colorectal (CRC)--with that of a rare malignant disease--Adrenocortical Carcinoma (ACC). As the analysis indicates, the spatial distribution of ACC is more likely to be related to hereditary factors than to environmental causes, in accordance with current knowledge about this rare disease.

"To me it's just another tool to help understand the evidence": public health decision-makers' perceptions of the value of geographical information systems (GIS)

While geographical information systems (GIS) have applications in a range of diverse fields, they remain underused by decision-makers in health settings. Through analysis of data captured in semi-structured interviews, the paper explores four thematic areas (the ontological, power, functionality and collaboration discourses) to understand how GIS are perceived and valued by public health decision-makers. The findings suggest that although GIS are viewed as useful tools to inform decision-making, they are in no way a panacea for practice. Participants' concerns that GIS outputs can potentially be misinterpreted or used erroneously might partly explain resistance to their use. GIS are, therefore, likely to be most effective in decision-making when applied in a multi-disciplinary context to facilitate sharing of data, knowledge and expertise across the public health landscape.

A Spatial-Temporal Approach to Differentiate Epidemic Risk Patterns

The purpose of disease mapping is to find spatial clustering and identify risk areas and potential epidemic initiators. Rather than relying on plotting either the case number or incidence rate, this chapter proposes three temporal risk indices: the probability of case occurrence (how often did uneven cases occur), the duration of an epidemic (how long did cases persist), and the intensity of a transmission (were the case of chronological significance). By integrating the three indicators using the local indicator of spatial autocorrelation (LISA) statistic, this chapter intends to develop a novel approach for evaluating spatial-temporal relationships with different risk patterns in the 2002 dengue epidemic, the worst outbreak in the past sixty years. With this approach, not only are hypotheses generated through the mapping processes in furthering investigation, but also procedures provided to identify spatial health risk levels with temporal characteristics.

Estimating the effect of air pollution from a coal-fired power station on the development of children's pulmonary function

Using geographical information systems (GIS) tools, the present study analyzed the association between children's lung function development and their long-term exposure to air pollution. The study covered the cohort of 1492 schoolchildren living in the vicinity of a major coal-fired power station in the Hadera sub-district of Israel. In 1996 and 1999, the children underwent subsequent pulmonary function tests (PFT) (forced vital capacity (FVC) and forced expiratory volume during the first second (FEV(1))), and the children's parents completed a detailed questionnaire on their health status and household characteristics. A negative association was found between changes in the results of PFT and the estimated individual levels of air pollution. A sensitivity test revealed a FEV(1) decline from -4.3% for the average pollution level to -10.2% for the high air pollution level. The results of a sensitivity test for FVC were found to be similar. Association with the reported health status was found to be insignificant. As we conclude, air pollution from a coal-fired power station, although not exceeding local pollution standards, had a negative effect on children's lung function development. As argued, previous studies carried out in the region failed to show the above association because they were based on zone approaches that assign average concentration levels of air pollutants to all individuals in each zone, leading to a misclassification bias of individual exposure.

On ecological fallacy, assessment errors stemming from misguided variable selection, and the effect of aggregation on the outcome of epidemiological study

In social and environmental sciences, ecological fallacy is an incorrect assumption about an individual based on aggregate data for a group. In the present study, the validity of this assumption was tested using both individual estimates of exposure to air pollution and aggregate data for 1,492 schoolchildren living in the in vicinity of a major coal-fired power station in the Hadera region of Israel. In 1996 and 1999, the children underwent subsequent pulmonary function tests (PFT), and their parents completed a detailed questionnaire on their health status and housing conditions. The association between children's PFT results and their exposure to air pollution was investigated in two phases. During the first phase, PFT averages were compared with average levels of air pollution detected in townships, and small census areas in which the children reside. During the second phase, individual pollution estimates were compared with individual PFT results, and pattern detection techniques (Getis-Ord statistic) were used to investigate the spatial data structure. While different levels of areal data aggregation changed the results only marginally, the choice of indices measuring the children's PFT performance had a significant influence on the outcome of the analysis. As argued, differences between individual-level and group-level effects of exposure (i.e., ecological or cross-level bias) are not necessary outcomes of data aggregation, and that seemingly unexpected results may often stem from a misguided selection of variables chosen to measure health effects. The implications of the results of the analysis for epidemiological studies are discussed, and recommendations for public health policy are formulated.

Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan

Mapping uneven events, such as disease cases or pollutants, is a basic but important procedure for analyzing regional relationships and variation in public health and environmental agencies. The purpose of mapping is to find out the spatial clustering of uneven events and identify spatial risk areas, which could lead to potential environmental hazards or epidemics. Meanwhile, more hypotheses could be generated through mapping process for further investigations. This paper proposed a novel spatial-temporal approach to focusing on: (1) how often these uneven cases occur, (2) how long these cases persist and (3) how significant cases occur in consecutive periods across the study area. The proposed model was applied to the dengue fever epidemic in Taiwan in 2002 as a case study, which was the worst epidemic in the last 60 years. This approach provides procedures to identify spatial health risk levels with temporal characteristics and assists in generating hypothesis that will be investigated in further detail.

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

Background

The purpose of this study is to examine the spatial and population (e.g., socio-economic) characteristics of low birthweight using two different cluster estimation techniques. We compared the results of Kulldorff's Spatial Scan Statistic with the results of Rushton's Spatial filtering technique across increasing sizes of spatial filters (circle). We were able to demonstrate that varying approaches exist to explore spatial variation in patterns of low birth weight.

Results

Spatial filtering results did not show any particular area that was not statistically significant based on SaTScan. The high rates, which remain as the filter size increases to 0.4, 0.5 to 0.6 miles, respectively, indicate that these differences are less likely due to chance. The maternal characteristics of births within clusters differed considerably between the two methods. Progressively larger Spatial filters removed local spatial variability, which eventually produced an approximate uniform pattern of low birth weight.

Conclusion

SaTScan and Spatial filtering cluster estimation methods produced noticeably different results from the same individual level birth data. SaTScan clusters are likely to differ from Spatial filtering clusters in terms of population characteristics and geographic area within clusters. Using the two methods in conjunction could provide more detail about the population and spatial features contained with each type of cluster.

Using GIS-Based Approaches to Support Research on Neurotoxicants and Other Children's Environmental Health Threats

Environmental threats to children's health are complex and multifaceted; consequently, children's environmental health research strives to identify areas of elevated exposure, determine whether particular demographic groups are inequitably exposed, and link exposures to incidence of disease. Many environmental health researchers use geographic information systems (GIS) to ex post display the results of their data collection and analysis. This methodological paper shows some ways by which the ex ante integration of GIS into environmental exposure and epidemiological research can significantly enhance: research design; sampling, recruitment, and retention strategies; data management and analysis; and community translation.